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github | ws15code/prob-trans-master | importEEGAfterICA.m | .m | prob-trans-master/EEG/code/importEEGAfterICA.m | 2,239 | utf_8 | ae4356b9964020591fd571f136221f2c | % The function importAfterICA loads the ICA processed data, breaks it
% into trials, and save the eegData.
function importEEGAfterICA(modelParams, subjectsIdx)
conditionLabel = {'noTask';'task'};
for subIndex=subjectsIdx
subIndex
% Loading eeg data after ICA
filenameExp = [model... |
github | ws15code/prob-trans-master | epochsAvg_pilot2.m | .m | prob-trans-master/EEG/code/epochsAvg_pilot2.m | 2,859 | utf_8 | a686e47ca6e8099815608ab8f3856c31 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function epocsResult = epochsAvg_pilot2(mode... |
github | ws15code/prob-trans-master | preprocessEnv.m | .m | prob-trans-master/EEG/code/preprocessEnv.m | 1,787 | utf_8 | 4612f5430d13135e275a09b11ba06d06 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: Vocoding Project
%
% This code extracts from the speech data its amplitude envelope. The
% Hilbert transform is used to get the analytical (complex) signal of the
% speech waveform. The amplitude of the analytical signal is referred as
% 'amplitude envelop... |
github | ws15code/prob-trans-master | trainCanon_3.m | .m | prob-trans-master/EEG/code/trainCanon_3.m | 6,390 | utf_8 | 04b78a649a6b9056f3a358aa97da7a1c | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function trainCanonVar = trainCanon_3(modelParams, subje... |
github | ws15code/prob-trans-master | noiseRemovalExport.m | .m | prob-trans-master/EEG/code/noiseRemovalExport.m | 6,247 | utf_8 | 99a50f35ac737c813b0c0d79190f4f59 | %% DSS export - eye blink and muscolar activity noise removal: this
% noise should be condition independent, therefore we concatenate all the
% trials and all the conditions
% This function downsamples the EEG data before the exportation
function noiseRemovalExport(modelParams, subjectsIdx, applyDss)
rejectedChanne... |
github | ws15code/prob-trans-master | preprocessEEG_pilot3.m | .m | prob-trans-master/EEG/code/preprocessEEG_pilot3.m | 4,900 | utf_8 | c5d0a8f18256c8f757a2d01fc34ad099 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15 - pilot 2
%
% This code performs the preprocessing of the EEG data
function modelParams = preprocessEEG_pilot3(modelParams, subjectsIdx, rejectedChannels)
load(['filters/' modelParams.bandPassfilter]);
conditionLabel = {'part1';'part2'};
... |
github | ws15code/prob-trans-master | preprocessEEG_pilot3UW.m | .m | prob-trans-master/EEG/code/preprocessEEG_pilot3UW.m | 5,227 | utf_8 | b8f121bc1fc3f694f7b49035b2e398f8 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15 - pilot 2
%
% This code performs the preprocessing of the EEG data
function modelParams = preprocessEEG_pilot3(modelParams, subjectsIdx, rejectedChannels)
load(['filters/' modelParams.bandPassfilter]);
conditionLabel = 'fwd_rev';
for... |
github | ws15code/prob-trans-master | preprocessSgram.m | .m | prob-trans-master/EEG/code/preprocessSgram.m | 2,471 | utf_8 | 27a11b72774b93875b16446b8137e79a | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: Vocoding Project
%
% This code extracts from the speech data its amplitude envelope. The
% Hilbert transform is used to get the analytical (complex) signal of the
% speech waveform. The amplitude of the analytical signal is referred as
% 'amplitude envelop... |
github | ws15code/prob-trans-master | importEEGAfterICA_3.m | .m | prob-trans-master/EEG/code/importEEGAfterICA_3.m | 2,241 | utf_8 | 23c297485f19a9c803687f9f78cd9dff | % The function importAfterICA loads the ICA processed data, breaks it
% into trials, and save the eegData.
function importEEGAfterICA_3(modelParams, subjectsIdx)
conditionLabel = {'part1';'part2'};
for subIndex=subjectsIdx
subIndex
% Loading eeg data after ICA
filenameExp = [mod... |
github | ws15code/prob-trans-master | trainEnv.m | .m | prob-trans-master/EEG/code/trainEnv.m | 6,176 | utf_8 | 04c98be0a75634f97030742934904967 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function [trainData, modelParams] = trainEnv(modelParams... |
github | ws15code/prob-trans-master | epochsAvgPre_pilot3.m | .m | prob-trans-master/EEG/code/epochsAvgPre_pilot3.m | 2,999 | utf_8 | ae91a48851f2dc5400d5d27d9065e4bf | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function epocsResult = epochsAvgPre_pilot3(m... |
github | ws15code/prob-trans-master | preprocessEEG.m | .m | prob-trans-master/EEG/code/preprocessEEG.m | 6,281 | utf_8 | 54913eca35e05b157640d9aaea2249b3 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15 - pilot 1
%
% This code performs the preprocessing of the EEG data
function modelParams = preprocessEEG(modelParams, subjectsIdx, rejectedChannels)
load(['filters/' modelParams.bandPassfilter]);
conditionLabel = {'slow';'fast'};
for ... |
github | ws15code/prob-trans-master | preprocessEEG_pilot2.m | .m | prob-trans-master/EEG/code/preprocessEEG_pilot2.m | 4,900 | utf_8 | 048b7cebe725988568ee063ee3aa8507 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15 - pilot 2
%
% This code performs the preprocessing of the EEG data
function modelParams = preprocessEEG_pilot2(modelParams, subjectsIdx, rejectedChannels)
load(['filters/' modelParams.bandPassfilter]);
conditionLabel = {'noTask';'task'};
... |
github | ws15code/prob-trans-master | epochsAvgPre.m | .m | prob-trans-master/EEG/code/epochsAvgPre.m | 2,884 | utf_8 | 4ad2387c19eadbc2129e0b2c52bd650b | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function epocsResult = epochsAvg(modelParams... |
github | ws15code/prob-trans-master | simpleClassification_old.m | .m | prob-trans-master/EEG/code/simpleClassification_old.m | 10,536 | utf_8 | 883a029bf6d3e8476b7a76f266000477 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function classificationResult = simpleClassi... |
github | ws15code/prob-trans-master | exportEEG4ICA.m | .m | prob-trans-master/EEG/code/exportEEG4ICA.m | 3,368 | utf_8 | 01a0bf594231cb97ab2cdd5a554463d7 | %% ICA export - eye blink and muscolar activity noise removal: this
% noise should be condition independent, therefore we concatenate all the
% trials and all the conditions
% This function downsamples the EEG data before the exportation
function exportEEG4ICA(modelParams, subjectsIdx)
conditionLabel = {'noTask';'t... |
github | ws15code/prob-trans-master | noiseRemovalExport_3UW.m | .m | prob-trans-master/EEG/code/noiseRemovalExport_3UW.m | 5,971 | utf_8 | 5c19d1ec7cbc8370a36a7cb06596544e | %% DSS export - eye blink and muscolar activity noise removal: this
% noise should be condition independent, therefore we concatenate all the
% trials and all the conditions
% This function downsamples the EEG data before the exportation
function noiseRemovalExport_3UW(modelParams, subjectsIdx, applyDss)
rejectedCh... |
github | ws15code/prob-trans-master | preprocessEEG_pilot1.m | .m | prob-trans-master/EEG/code/preprocessEEG_pilot1.m | 6,299 | utf_8 | 9995cd87c7209468bffc1b29dc0301a7 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15 - pilot 2 - no-task/2-backTask
%
% This code performs the preprocessing of the EEG data
function modelParams = preprocessEEG_pilot1(modelParams, subjectsIdx, rejectedChannels)
load(['filters/' modelParams.bandPassfilter]);
conditionLabel = {'... |
github | ws15code/prob-trans-master | simpleClassification_3.m | .m | prob-trans-master/EEG/code/simpleClassification_3.m | 11,768 | utf_8 | f4c85d1a05cbfda9fa040c7e55c5aa78 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function classificationResult = simpleClassi... |
github | ws15code/prob-trans-master | simpleClassification_3oldNoPool.m | .m | prob-trans-master/EEG/code/simpleClassification_3oldNoPool.m | 11,371 | utf_8 | 9dd1da79f2f49d1005051910b6020d6e | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function classificationResult = simpleClassi... |
github | ws15code/prob-trans-master | simpleClassification_3UW.m | .m | prob-trans-master/EEG/code/simpleClassification_3UW.m | 22,834 | utf_8 | 9ada6022fe995b6ea866c1bb170142bc | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function classificationResult = simpleClassi... |
github | ws15code/prob-trans-master | simpleClassification.m | .m | prob-trans-master/EEG/code/simpleClassification.m | 11,583 | utf_8 | 90b61dd3f0aa40e3b54ad28f340b1ba5 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function classificationResult = simpleClassi... |
github | ws15code/prob-trans-master | trainCanon.m | .m | prob-trans-master/EEG/code/trainCanon.m | 7,673 | utf_8 | 46bb1a5598ce152f64bbce837bb84768 | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function trainCanonVar = trainCanon(modelParams, subject... |
github | ws15code/prob-trans-master | noiseRemovalExport_3.m | .m | prob-trans-master/EEG/code/noiseRemovalExport_3.m | 6,247 | utf_8 | 181062711be4b46179ce05b7c79b64e5 | %% DSS export - eye blink and muscolar activity noise removal: this
% noise should be condition independent, therefore we concatenate all the
% trials and all the conditions
% This function downsamples the EEG data before the exportation
function noiseRemovalExport(modelParams, subjectsIdx, applyDss)
rejectedChanne... |
github | ws15code/prob-trans-master | globalfieldpower.m | .m | prob-trans-master/EEG/code/libs/globalfieldpower.m | 276 | utf_8 | 499cc6da030b1ad4cd9819362317d5c7 | % This is a function to calculate the global field power a la Skrandies.
% Written by Ed Lalor. 24/06/08
function [gfp] = globalfieldpower(EEG)
gfp = zeros(1, size(EEG,2));
for i = 1:size(EEG,2)
gfp(i) = sqrt(sum((EEG(:,i) - mean(EEG(:,i))).^2));
end
end |
github | ws15code/prob-trans-master | speech2spectro.m | .m | prob-trans-master/EEG/code/libs/speech2spectro.m | 1,162 | utf_8 | ca9ef4b9849d3d6621d34586f78c48e4 | % Preprocess function given the audio files in the folder 'audioPath'
% and the bandpass filters in 'STRFfilters'
% >> speech2spectro('./STRFfilters',speechEnvelope', 512, 512)
function STRFstimuli = speech2spectro(STRFfilters, auData, auFreq, fs)
load(STRFfilters)
for i = 1:length(Hd)
disp(['Band'... |
github | ws15code/prob-trans-master | time2samp.m | .m | prob-trans-master/EEG/code/libs/time2samp.m | 162 | utf_8 | e05feb59a03e50b7829c85891b42672a | %% Conversion local functions samples-time[ms] %%
% Time to sample point, given the sampling frequency FS
function ret = time2samp(time,FS)
ret = time/1000 * FS; |
github | ws15code/prob-trans-master | samp2time.m | .m | prob-trans-master/EEG/code/libs/samp2time.m | 163 | utf_8 | c7d5757ed8adcadd99130a83782c5d99 | %% Conversion local functions samples-time[ms] %%
% Sample to time point, given the sampling frequency FS
function ret = samp2time(samp,FS)
ret = samp/FS * 1000;
|
github | ws15code/prob-trans-master | shift.m | .m | prob-trans-master/EEG/code/libs/shift.m | 635 | utf_8 | f617d4e3b367d3a0163431d034a2faeb | % shift
% shiftedMat = shift(mat, pos) shifts the values in the matrix 'mat' by
% 'pos' elements along the first dimension. The returned matrix
% will have zero-padding on the top or bottom part, if pos is positive or
% negative respectively.
%
% Inputs:
% mat - data matrix
% pos - number ... |
github | ws15code/prob-trans-master | pdftops.m | .m | prob-trans-master/EEG/code/libs/export_fig/pdftops.m | 3,188 | utf_8 | eb7ee38e8e5d4b47aab7a7aa9d2208c4 | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for futur... |
github | ws15code/prob-trans-master | isolate_axes.m | .m | prob-trans-master/EEG/code/libs/export_fig/isolate_axes.m | 3,794 | utf_8 | b104d66dd4d36f35d275c4ef3d2f41cd | %ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
% specified must all be in th... |
github | ws15code/prob-trans-master | pdf2eps.m | .m | prob-trans-master/EEG/code/libs/export_fig/pdf2eps.m | 1,525 | utf_8 | 9b46432b206f5e1d2f60051a8fb16247 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.c... |
github | ws15code/prob-trans-master | print2array.m | .m | prob-trans-master/EEG/code/libs/export_fig/print2array.m | 6,474 | utf_8 | 4ead930267fe61c9b2a87139ee559dc8 | %PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% [A bcol] = print2array(...)
%
% This function outputs a bitmap image of the given fig... |
github | ws15code/prob-trans-master | eps2pdf.m | .m | prob-trans-master/EEG/code/libs/export_fig/eps2pdf.m | 5,149 | utf_8 | a76c49a222381133a3c337333bf6a983 | %EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
%
% This function converts an eps file... |
github | ws15code/prob-trans-master | copyfig.m | .m | prob-trans-master/EEG/code/libs/export_fig/copyfig.m | 846 | utf_8 | 8f479727f76b878a077b76ca7afed48e | %COPYFIG Create a copy of a figure, without changing the figure
%
% Examples:
% fh_new = copyfig(fh_old)
%
% This function will create a copy of a figure, but not change the figure,
% as copyobj sometimes does, e.g. by changing legends.
%
% IN:
% fh_old - The handle of the figure to be copied. Default: gc... |
github | ws15code/prob-trans-master | user_string.m | .m | prob-trans-master/EEG/code/libs/export_fig/user_string.m | 2,462 | utf_8 | dd1a7fa5b4f2be6320fc2538737a2f3e | %USER_STRING Get/set a user specific string
%
% Examples:
% string = user_string(string_name)
% saved = user_string(string_name, new_string)
%
% Function to get and set a string in a system or user specific file. This
% enables, for example, system specific paths to binaries to be saved.
%
% IN:
% string_name - ... |
github | ws15code/prob-trans-master | export_fig.m | .m | prob-trans-master/EEG/code/libs/export_fig/export_fig.m | 30,730 | utf_8 | 376baa9aac77137ddab15546dd1d11b6 | %EXPORT_FIG Exports figures suitable for publication
%
% Examples:
% im = export_fig
% [im alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% export_fig ... -native
% export_fig ... -m<val>
% export_fig... |
github | ws15code/prob-trans-master | ghostscript.m | .m | prob-trans-master/EEG/code/libs/export_fig/ghostscript.m | 5,169 | utf_8 | 6c4ddcf9e83cbc0d50d5ecd1921a1350 | %GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is stored for future reference.
% ... |
github | ljstrnadiii/3D_TDA-master | colorrr.m | .m | 3D_TDA-master/colorrr.m | 350 | utf_8 | 3f0b600d7c3337d4e9d5cd3afa236545 |
function clrs = colorrr(n)
% color map with red and blue on the edges
C = [1 0 0;0 0 1];
% convert to HSV for interpolation
C_HSV = rgb2hsv(C);
% interpolate hue value
C_HSV_interp = interp1([0 n], C_HSV(:, 1), 1:n);
% compose full HSV colormap
C_HSV = [C_HSV_interp(:), repmat(C_HSV(2:3), n, 1)];
% convert back to RG... |
github | alexalex222/Wind-Speed-Prediction-master | emgm.m | .m | Wind-Speed-Prediction-master/Multimodel Code/emgm.m | 3,012 | utf_8 | 3e33f09eae2378fb4c43bac397dcedbf | function [label, model, llh] = emgm(X, init)
% Perform EM algorithm for fitting the Gaussian mixture model.
% X: d x n data matrix
% init: k (1 x 1) or label (1 x n, 1<=label(i)<=k) or center (d x k)
% Written by Michael Chen (sth4nth@gmail.com).
%% initialization
fprintf('EM for Gaussian mixture: running ... \n');... |
github | alexalex222/Wind-Speed-Prediction-master | f1.m | .m | Wind-Speed-Prediction-master/SVR-Kalman/f1.m | 408 | utf_8 | 846533f6236754c7362829983348b666 | % Dynamical model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_n = f1(x)
load('regressionmo... |
github | alexalex222/Wind-Speed-Prediction-master | emgm.m | .m | Wind-Speed-Prediction-master/Multimodel Seasonal Code/emgm.m | 3,012 | utf_8 | 3e33f09eae2378fb4c43bac397dcedbf | function [label, model, llh] = emgm(X, init)
% Perform EM algorithm for fitting the Gaussian mixture model.
% X: d x n data matrix
% init: k (1 x 1) or label (1 x n, 1<=label(i)<=k) or center (d x k)
% Written by Michael Chen (sth4nth@gmail.com).
%% initialization
fprintf('EM for Gaussian mixture: running ... \n');... |
github | alexalex222/Wind-Speed-Prediction-master | f1.m | .m | Wind-Speed-Prediction-master/UKF/UKF/f1.m | 402 | utf_8 | f03e846f88a1acd0049465c5fb83d6ae | % Dynamical model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_n = f1(x)
x_n(1,1)=0.63*x(1,... |
github | alexalex222/Wind-Speed-Prediction-master | h1.m | .m | Wind-Speed-Prediction-master/UKF/UKF/h1.m | 397 | utf_8 | 5dfb3b114c63aac28e99ddd62a936dd9 | % Measurement model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function Y = h1(x)
f = x(1,:);
a = ... |
github | alexalex222/Wind-Speed-Prediction-master | ekf_predict2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ekf_predict2.m | 3,343 | UNKNOWN | e361d1331b5970202696843b127cb7e9 | %EKF_PREDICT2 2nd order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT2(M,P,[A,F,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% A - Derivative of a() with respect to state as
% matrix, inline function, function ... |
github | alexalex222/Wind-Speed-Prediction-master | ut_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ut_transform.m | 3,564 | UNKNOWN | f75fd5abda99dff018cbeec9e7061f9f | %UT_TRANSFORM Perform unscented transform
%
% Syntax:
% [mu,S,C,X,Y,w] = UT_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% matrix, inline function, fu... |
github | alexalex222/Wind-Speed-Prediction-master | quad_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/quad_transform.m | 2,486 | UNKNOWN | c1cc63e44335d77b4d42a0e5b2732cb5 | %UT_TRANSFORM Perform quadratic approximation based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = QUAD_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,para... |
github | alexalex222/Wind-Speed-Prediction-master | uimm_predict.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/uimm_predict.m | 3,833 | utf_8 | c3e69773e05f6724c702dc85eebd5e72 | %IMM_PREDICT UKF based Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = UIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% ... |
github | alexalex222/Wind-Speed-Prediction-master | imm_filter.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/imm_filter.m | 4,152 | utf_8 | 71128ac12e721c163f0c582ff2af4e6d | %IMM_FILTER Interacting Multiple Model (IMM) Filter prediction and update steps
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_FILTER(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q,Y,H,R)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_update1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ukf_update1.m | 3,047 | UNKNOWN | c6fa396ccaa417dcc9a2e8bfebc304a5 | %UKF_UPDATE1 - Additive form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = UKF_UPDATE1(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model functio... |
github | alexalex222/Wind-Speed-Prediction-master | imm_smooth.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/imm_smooth.m | 8,587 | utf_8 | 299bb594c62b9fe2b393c54e2dfd9625 | %IMM_SMOOTH Fixed-interval IMM smoother using two IMM-filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = IMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,Q,R,H,Y)
%
% In:
% MM - NxK matrix containing the means of forward-time
% IMM-filter on each time step
% PP - NxNxK matrix containing the c... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict3.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ukf_predict3.m | 2,671 | UNKNOWN | f53661137ceedfac68cf499295cec7a8 | %UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step
%
% Syntax:
% [M,P,X,w] = UKF_PREDICT3(M,P,f,Q,R,f_param,alpha,beta,kappa)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% ... |
github | alexalex222/Wind-Speed-Prediction-master | kf_loop.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/kf_loop.m | 1,888 | utf_8 | 0db0b34d194f33879dab4a27c7fbf0da | %KF_LOOP Performs the prediction and update steps of the Kalman filter
% for a set of measurements.
%
% Syntax:
% [MM,PP] = KF_LOOP(X,P,H,R,Y,A,Q)
%
% In:
% X - Nx1 initial estimate for the state mean
% P - NxN initial estimate for the state covariance
% H - DxN measurement matrix
% R - DxD meas... |
github | alexalex222/Wind-Speed-Prediction-master | imm_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/imm_update.m | 2,533 | utf_8 | 860499b62caf6e4b660ec41b713d4e0e | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j state covari... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_update3.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ukf_update3.m | 3,076 | UNKNOWN | d6a6f273f4495654caee03c01ff019e8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE3(M,P,Y,h,R,X,w,h_param,alpha,beta,kappa,mat,sigmas)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement... |
github | alexalex222/Wind-Speed-Prediction-master | ut_weights.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ut_weights.m | 1,585 | UNKNOWN | 5ae13a5f605593674a60c12f8f55bbe8 | %UT_WEIGHTS - Generate unscented transformation weights
%
% Syntax:
% [WM,WC,c] = ut_weights(n,alpha,beta,kappa)
%
% In:
% n - Dimensionality of random variable
% alpha - Transformation parameter (optional, default 0.5)
% beta - Transformation parameter (optional, default 2)
% kappa - Transformation pa... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ukf_predict2.m | 2,307 | UNKNOWN | da4d237360c2ea17a88a15b18de0a65d | %UKF_PREDICT2 Augmented (state and process noise) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT2(M,P,a,Q,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% function handle ... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ukf_predict1.m | 2,297 | UNKNOWN | e470b6641ff341b56241835f6b908dd3 | %UKF_PREDICT1 Nonaugmented (Additive) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT1(M,P,f,Q,f_param,alpha,beta,kappa,mat)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as a matrix A defining
% linear function a(x) ... |
github | alexalex222/Wind-Speed-Prediction-master | imm_predict.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/imm_predict.m | 3,459 | utf_8 | a557c545bef965afe036ee2e59f5274b | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = IMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - Cell arra... |
github | alexalex222/Wind-Speed-Prediction-master | eimm_predict.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/eimm_predict.m | 4,185 | utf_8 | 0e65c7ab1a1e1717c2e76e785f151d22 | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = EIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - ... |
github | alexalex222/Wind-Speed-Prediction-master | uimm_smooth.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/uimm_smooth.m | 9,008 | utf_8 | f165a3da19e62f9a637fc4caac8ad45f | %UIMM_SMOOTH UKF based Fixed-interval IMM smoother using two IMM-UKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = UIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM... |
github | alexalex222/Wind-Speed-Prediction-master | eimm_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/eimm_update.m | 3,594 | utf_8 | 2be8defc9605899d693c67d6496abc39 | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,h,R,param)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j stat... |
github | alexalex222/Wind-Speed-Prediction-master | urts_smooth1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/urts_smooth1.m | 3,687 | UNKNOWN | 3e5bdb3c14f06c08d0f41cf62ad81e82 | %URTS_SMOOTH1 Additive form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,D] = URTS_SMOOTH1(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic m... |
github | alexalex222/Wind-Speed-Prediction-master | lin_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/lin_transform.m | 1,798 | UNKNOWN | a145a61ccd833f3d7100c7d651dfb779 | %UT_TRANSFORM Perform linearization based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = LIN_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% ... |
github | alexalex222/Wind-Speed-Prediction-master | ckf_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ckf_update.m | 2,193 | utf_8 | 2b4cf6b96fb1094a10fea58afc4df3c3 |
function [M,P,K,MU,S,LH] = ckf_update(M,P,Y,h,R,h_param)
% CKF_UPDATE - Cubature Kalman filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = CKF_UPDATE(M,P,Y,h,R,param)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measu... |
github | alexalex222/Wind-Speed-Prediction-master | uimm_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/uimm_update.m | 2,973 | utf_8 | e386b64264ff328d4c5e5fa35c945487 | %IMM_UPDATE UKF based Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j st... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_update2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/ukf_update2.m | 3,187 | UNKNOWN | 0a303ec8bcb980daf834c0d75ff65fb8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE2(M,P,Y,h,R,h_param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model func... |
github | alexalex222/Wind-Speed-Prediction-master | urts_smooth2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/urts_smooth2.m | 3,098 | UNKNOWN | eafbc7975b06ca22a4d6002269610046 | %URTS_SMOOTH2 Augmented form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,S] = URTS_SMOOTH2(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic ... |
github | alexalex222/Wind-Speed-Prediction-master | eimm_smooth.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/eimm_smooth.m | 10,081 | utf_8 | 14cc37296d6aa91b1c0ded61dd76c73f | %EIMM_SMOOTH EKF based fixed-interval IMM smoother using two IMM-EKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = EIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM-f... |
github | alexalex222/Wind-Speed-Prediction-master | ut_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/windspeed/ut_transform.m | 3,564 | UNKNOWN | f75fd5abda99dff018cbeec9e7061f9f | %UT_TRANSFORM Perform unscented transform
%
% Syntax:
% [mu,S,C,X,Y,w] = UT_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% matrix, inline function, fu... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_update1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/windspeed/ukf_update1.m | 3,047 | UNKNOWN | c6fa396ccaa417dcc9a2e8bfebc304a5 | %UKF_UPDATE1 - Additive form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = UKF_UPDATE1(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model functio... |
github | alexalex222/Wind-Speed-Prediction-master | ut_weights.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/windspeed/ut_weights.m | 1,585 | UNKNOWN | 5ae13a5f605593674a60c12f8f55bbe8 | %UT_WEIGHTS - Generate unscented transformation weights
%
% Syntax:
% [WM,WC,c] = ut_weights(n,alpha,beta,kappa)
%
% In:
% n - Dimensionality of random variable
% alpha - Transformation parameter (optional, default 0.5)
% beta - Transformation parameter (optional, default 2)
% kappa - Transformation pa... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/windspeed/ukf_predict1.m | 2,297 | UNKNOWN | e470b6641ff341b56241835f6b908dd3 | %UKF_PREDICT1 Nonaugmented (Additive) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT1(M,P,f,Q,f_param,alpha,beta,kappa,mat)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as a matrix A defining
% linear function a(x) ... |
github | alexalex222/Wind-Speed-Prediction-master | f_turn.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/eimm_demo/f_turn.m | 924 | utf_8 | 7ef714a1030afbd53e53eab3ff996db5 | %
% A coordinated turn model for extended IMM filter demonstration
%
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_k = f_turn(x,param)
... |
github | alexalex222/Wind-Speed-Prediction-master | bot_d2h_dx2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/eimm_demo/bot_d2h_dx2.m | 991 | utf_8 | 121065207e815dd017245b07a9f807b6 | % Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = bot_d2h_dx2(x,s)
% Space for Hessia... |
github | alexalex222/Wind-Speed-Prediction-master | f_turn_inv.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/eimm_demo/f_turn_inv.m | 919 | utf_8 | 81845969367ed59c79f6f535a775dddc | %
% Inverse prediction for the coordinated turn model
% used in extended IMM filter demonstration
%
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
f... |
github | alexalex222/Wind-Speed-Prediction-master | ekf_predict2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ekf_predict2.m | 3,343 | UNKNOWN | e361d1331b5970202696843b127cb7e9 | %EKF_PREDICT2 2nd order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT2(M,P,[A,F,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% A - Derivative of a() with respect to state as
% matrix, inline function, function ... |
github | alexalex222/Wind-Speed-Prediction-master | ut_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ut_transform.m | 3,564 | UNKNOWN | f75fd5abda99dff018cbeec9e7061f9f | %UT_TRANSFORM Perform unscented transform
%
% Syntax:
% [mu,S,C,X,Y,w] = UT_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% matrix, inline function, fu... |
github | alexalex222/Wind-Speed-Prediction-master | quad_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/quad_transform.m | 2,486 | UNKNOWN | c1cc63e44335d77b4d42a0e5b2732cb5 | %UT_TRANSFORM Perform quadratic approximation based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = QUAD_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,para... |
github | alexalex222/Wind-Speed-Prediction-master | uimm_predict.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/uimm_predict.m | 3,833 | utf_8 | c3e69773e05f6724c702dc85eebd5e72 | %IMM_PREDICT UKF based Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = UIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% ... |
github | alexalex222/Wind-Speed-Prediction-master | imm_filter.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/imm_filter.m | 4,152 | utf_8 | 71128ac12e721c163f0c582ff2af4e6d | %IMM_FILTER Interacting Multiple Model (IMM) Filter prediction and update steps
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_FILTER(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q,Y,H,R)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_update1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ukf_update1.m | 3,047 | UNKNOWN | c6fa396ccaa417dcc9a2e8bfebc304a5 | %UKF_UPDATE1 - Additive form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = UKF_UPDATE1(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model functio... |
github | alexalex222/Wind-Speed-Prediction-master | imm_smooth.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/imm_smooth.m | 8,587 | utf_8 | 299bb594c62b9fe2b393c54e2dfd9625 | %IMM_SMOOTH Fixed-interval IMM smoother using two IMM-filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = IMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,Q,R,H,Y)
%
% In:
% MM - NxK matrix containing the means of forward-time
% IMM-filter on each time step
% PP - NxNxK matrix containing the c... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict3.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ukf_predict3.m | 2,671 | UNKNOWN | f53661137ceedfac68cf499295cec7a8 | %UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step
%
% Syntax:
% [M,P,X,w] = UKF_PREDICT3(M,P,f,Q,R,f_param,alpha,beta,kappa)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% ... |
github | alexalex222/Wind-Speed-Prediction-master | kf_loop.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/kf_loop.m | 1,888 | utf_8 | 0db0b34d194f33879dab4a27c7fbf0da | %KF_LOOP Performs the prediction and update steps of the Kalman filter
% for a set of measurements.
%
% Syntax:
% [MM,PP] = KF_LOOP(X,P,H,R,Y,A,Q)
%
% In:
% X - Nx1 initial estimate for the state mean
% P - NxN initial estimate for the state covariance
% H - DxN measurement matrix
% R - DxD meas... |
github | alexalex222/Wind-Speed-Prediction-master | ekf_sine_d2h_dx2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ekf_sine_d2h_dx2.m | 473 | utf_8 | 6a5d46b28c8d2b088180bbe20bc81c1b | % Hessian of the measurement model function in the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function df = ekf_sine_d2h_... |
github | alexalex222/Wind-Speed-Prediction-master | imm_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/imm_update.m | 2,533 | utf_8 | 860499b62caf6e4b660ec41b713d4e0e | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j state covari... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_update3.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ukf_update3.m | 3,076 | UNKNOWN | d6a6f273f4495654caee03c01ff019e8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE3(M,P,Y,h,R,X,w,h_param,alpha,beta,kappa,mat,sigmas)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement... |
github | alexalex222/Wind-Speed-Prediction-master | ut_weights.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ut_weights.m | 1,585 | UNKNOWN | 5ae13a5f605593674a60c12f8f55bbe8 | %UT_WEIGHTS - Generate unscented transformation weights
%
% Syntax:
% [WM,WC,c] = ut_weights(n,alpha,beta,kappa)
%
% In:
% n - Dimensionality of random variable
% alpha - Transformation parameter (optional, default 0.5)
% beta - Transformation parameter (optional, default 2)
% kappa - Transformation pa... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict2.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ukf_predict2.m | 2,307 | UNKNOWN | da4d237360c2ea17a88a15b18de0a65d | %UKF_PREDICT2 Augmented (state and process noise) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT2(M,P,a,Q,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% function handle ... |
github | alexalex222/Wind-Speed-Prediction-master | ukf_predict1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ukf_predict1.m | 2,297 | UNKNOWN | e470b6641ff341b56241835f6b908dd3 | %UKF_PREDICT1 Nonaugmented (Additive) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT1(M,P,f,Q,f_param,alpha,beta,kappa,mat)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as a matrix A defining
% linear function a(x) ... |
github | alexalex222/Wind-Speed-Prediction-master | ekf_sine_h.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ekf_sine_h.m | 411 | utf_8 | 4636d4ebcbeacdd8a0dd352e576511d2 | % Measurement model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function Y = ekf_sine_h(x,param)
f = x... |
github | alexalex222/Wind-Speed-Prediction-master | ekf_sine_dh_dx.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ekf_sine_dh_dx.m | 420 | utf_8 | d75fee0ef60b44bc6f56b32735b4b1ea | % Jacobian of the measurement model function in the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = ekf_sine_dh_... |
github | alexalex222/Wind-Speed-Prediction-master | imm_predict.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/imm_predict.m | 3,459 | utf_8 | a557c545bef965afe036ee2e59f5274b | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = IMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - Cell arra... |
github | alexalex222/Wind-Speed-Prediction-master | eimm_predict.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/eimm_predict.m | 4,185 | utf_8 | 0e65c7ab1a1e1717c2e76e785f151d22 | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = EIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - ... |
github | alexalex222/Wind-Speed-Prediction-master | uimm_smooth.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/uimm_smooth.m | 9,008 | utf_8 | f165a3da19e62f9a637fc4caac8ad45f | %UIMM_SMOOTH UKF based Fixed-interval IMM smoother using two IMM-UKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = UIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM... |
github | alexalex222/Wind-Speed-Prediction-master | eimm_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/eimm_update.m | 3,594 | utf_8 | 2be8defc9605899d693c67d6496abc39 | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,h,R,param)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j stat... |
github | alexalex222/Wind-Speed-Prediction-master | urts_smooth1.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/urts_smooth1.m | 3,687 | UNKNOWN | 3e5bdb3c14f06c08d0f41cf62ad81e82 | %URTS_SMOOTH1 Additive form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,D] = URTS_SMOOTH1(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic m... |
github | alexalex222/Wind-Speed-Prediction-master | lin_transform.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/lin_transform.m | 1,798 | UNKNOWN | a145a61ccd833f3d7100c7d651dfb779 | %UT_TRANSFORM Perform linearization based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = LIN_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% ... |
github | alexalex222/Wind-Speed-Prediction-master | ckf_update.m | .m | Wind-Speed-Prediction-master/UKF/unscented kalman/ekfukf/demos/ekf_sine_demo/ckf_update.m | 2,193 | utf_8 | 2b4cf6b96fb1094a10fea58afc4df3c3 |
function [M,P,K,MU,S,LH] = ckf_update(M,P,Y,h,R,h_param)
% CKF_UPDATE - Cubature Kalman filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = CKF_UPDATE(M,P,Y,h,R,param)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measu... |
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