matlab-(含教程)基于ANN人工神经网络的MPC控制器训练优化过程matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); Input_8_Features = 1; % A logical parameter to choose/call the input features with a lenght of 9. Input_8_Features_withOneDelay = 0; % A logical parameter to choose/call the input features with a lenght of 17. Input_8_Features_with2Delays = 0; % A logical parameter to choose/call the input features with a lenght of 25. LinearLoad = 0; %% Load the Data Files. if LinearLoad == 0 % Case #1: Linear Loads Samples = 60; else % Case #2: Non-Linear Loads Samples = 70; end % To store the dataset InputFeaturesSamples = cell(1, Samples); TargetsSamples = cell(1, Samples); for i = 1:Samples if Input_8_Features myfilename = sprintf('Dataset/InputFeatures/Input_8Features/Sample%d.mat', i); myfilename1 = sprintf('Dataset/InputFeatures/Targets/Targets-8inputs/TargetSample%d.mat', i); elseif Input_8_Features_withOneDelay myfilename = sprintf('Dataset/InputFeatures/Input_8FeaturesWithDelay/OneDelaySamples/Sample%d.mat', i); myfilename1 = sprintf('Dataset/InputFeatures/Targets/TargetSample%d.mat', i); elseif Input_8_Features_with2Delays myfilename = sprintf('Dataset/InputFeatures/Input_8FeaturesWithDelay/TwoDelaysSamples/Sample%d.mat', i); myfilename1 = sprintf('Dataset/InputFeatures/Targets/TargetSample%d.mat', i); end InputFeaturesSamples{i} = importdata(myfilename); TargetsSamples{i} = importdata(myfilename1); end T = cell2mat(TargetsSamples); X = cell2mat(InputFeaturesSamples); X = X((1:size(X,1)-1),:); x = X; t = T; trainFcn = 'trainscg'; hiddenLayerSize = 15; net = patternnet(hiddenLayerSize, trainFcn); net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Train the Network [net,tr] = train(net,x,t); % Test the Network y = net(x); e = gsubtract(t,y); performance = perform(net,t,y); tind = vec2ind(t); yind = vec2ind(y); percentErrors = sum(tind ~= yind)/numel(tind); view(net) gensim(net) figure, plotperform(tr) figure, plottrainstate(tr) figure, ploterrhist(e) figure, plotconfusion(t,y) figure, plotroc(t,y)