Commit 7b4ca3a9 authored by hazrmard's avatar hazrmard
Browse files

refactored matlab files in separate directory

parent 1d964d61

matlab/CompositeModel.m

0 → 100644
+27 −0
Original line number Diff line number Diff line 
classdef CompositeModel

    %COMPOSITEMODEL Combines black- and grey- box models.

    %   Detailed explanation goes here

    

    properties

        blackBox

        greyBox

        modelType

    end

    

    methods

        function obj = CompositeModel(blackBox, greyBox, modelType)

            %UNTITLED Construct an instance of this class

            %   Detailed explanation goes here

            obj.blackBox = blackBox;

            obj.greyBox = greyBox;

            obj.modelType = modelType;

        end

        

        function outputArg = method1(obj,inputArg)

            %METHOD1 Summary of this method goes here

            %   Detailed explanation goes here

            outputArg = obj.Property1 + inputArg;

        end

    end

end

matlab/PhysicalModels/genRCData.m

+3 −3
Original line number Diff line number Diff line
@@ -20,7 +20,7 @@ in = t' * peak / duration; 
% sim_in = [t', awgn(in, noise, 'measured')];

sim_in = [t', in];

data = runSim('RC', sim_in);

save("resRC_ramp_" + num2str(noise) + ".mat",...

save("RC_ramp_" + num2str(noise) + ".mat",...

    'data', 'peak', 'resolution');



% sine input
@@ -28,14 +28,14 @@ in = sin(2*pi*freq*t') * peak; 
% sim_in = [t', awgn(in, noise, 'measured')];

sim_in = [t', in];

data = runSim('RC', sim_in);

save("resRC_sin_" + num2str(noise) + ".mat",...

save("RC_sin_" + num2str(noise) + ".mat",...

    'data', 'freq', 'peak', 'resolution');



% combined ramp + sine + noise

in = (t' * peak / duration) + sin(2*pi*freq*t') * peak;

sim_in = [t', awgn(in, noise, 'measured')];

data = runSim('RC', sim_in);

save("resRC_comb_" + num2str(noise) +".mat",...

save("RC_comb_" + num2str(noise) +".mat",...

    'data', 'freq', 'peak', 'resolution');



clear data freq peak N t duration resolution in sim_in
 
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matlab/blackBoxRC.m

+3 −1
Original line number Diff line number Diff line
@@ -8,11 +8,12 @@ 
close all;



% Load training data

rcdat = load('resRC_comb_250.mat');

rcdat = load('RC_comb_250.mat');

t = rcdat.data(:,1);    % timestamps

u = rcdat.data(:,2);    % input

y = rcdat.data(:,3) * 1000;    % output



% Normalizing input signal

mu = mean(u);

sig = std(u);

stdU = (u - mu) / sig;
@@ -53,6 +54,7 @@ options = trainingOptions('adam', ... 


net = trainNetwork(XTrain, YTrain, layers, options);



% Plot predicted and actual output

YPred = cell2mat(predict(net, {u'}));

plot(t, y, t, YPred);

legend('Actual', 'Prediction');
 
 No newline at end of file

matlab/dummyLSTMseq.m

+28 −20
Original line number Diff line number Diff line 
% Training an LSTM network to predict a sequence

% of numbers following an input sequence.

% Training an LSTM network to predict the derivative

% of a sequence.

% 

% Input:  1,2,3

% Output: 2,3,4

% Input:  1,2,5,3

% Output: 1,1,3,-2

N = 1000;

NTest = 200;

seqLen = 10;



seq = reshape([1:N], seqLen, [])';

testSeq = reshape([N+1:N+NTest], seqLen, [])';



NTest = N;

seqLen = N;

range = 100;



rng(0);

numbers = randi([0, range], 1, N+NTest);

gradients = gradient(numbers);



seq = reshape(numbers(1:N), seqLen, [])';

seqY = reshape(gradients(1:N), seqLen, [])';

testSeq = reshape(numbers(N+1:end), seqLen, [])';

testSeqY = reshape(gradients(N+1:end), seqLen, [])';

% The input variables are normalized to have 0 mean

% and 1 variance.

mu = mean(seq(:));
@@ -23,9 +29,8 @@ XTest = mat2cell(stdTest, ones(1, NTest/seqLen)); 


% For format of the response, see

% https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html

% The labels are a cell array of sequences offset by 1

YTrain = mat2cell(seq + 1, ones(1, N/seqLen));

YTest = mat2cell(testSeq + 1, ones(1, NTest/seqLen));

YTrain = mat2cell(seqY, ones(1, N/seqLen));

YTest = mat2cell(testSeqY, ones(1, NTest/seqLen));



% Defining an LSTM network. An LSTM layer takes as

% input `numHiddenUnits` which is the number of time
@@ -33,21 +38,24 @@ YTest = mat2cell(testSeq + 1, ones(1, NTest/seqLen)); 
% over which the LSTM layer operates.

layers = [...

    sequenceInputLayer(1)

    lstmLayer(10,'OutputMode','sequence')

    lstmLayer(1,'OutputMode','sequence')

    lstmLayer(1,'OutputMode','sequence')

    fullyConnectedLayer(1)

    regressionLayer];



options = trainingOptions('adam', ...

    'MaxEpochs',150, ...

    'MaxEpochs',100, ...

    'MiniBatchSize', 5, ...

    'GradientThreshold',1, ...

    'InitialLearnRate',1.0, ...

    'InitialLearnRate',1, ...

    'LearnRateSchedule','piecewise', ...

    'LearnRateDropPeriod',20, ...

    'LearnRateDropFactor',0.5, ...

    'LearnRateDropPeriod',50, ...

    'LearnRateDropFactor',0.75, ...

    'Verbose',0, ...

    'Plots','training-progress');



net = trainNetwork(XTrain, YTrain, layers, options);



YPred = int32(cell2mat(predict(net, XTrain)));
 
 No newline at end of file
 
YPred = int32(cell2mat(predict(net, XTest)));

plot(1:NTest, YPred, 1:NTest, gradients(N+1:end));

legend('Predicted', 'Actual');
 
 No newline at end of file

matlab/greyBoxRC.m

+2 −2
Original line number Diff line number Diff line
@@ -13,7 +13,7 @@ par = {'resistance',500; 'capacitance', 1e-3}; 
sys_init = idgrey(@ssRC, par, 'c');

sys_init.Structure.Parameters(2).Free = false;



rcdat = load('resRC_comb.mat');

rcdat = load('RC_comb_25.mat');

t = rcdat.data(:,1);    % timestamps

u = rcdat.data(:,2);    % input

y = rcdat.data(:,3);    % output