通过PSO优化神经网络权值的matlab仿真+含代码操作演示视频

clc; clear; close all; warning off; addpath(genpath(pwd)); load('Weight_mat.mat') load('trainset.mat') load('testset.mat') var_num=71; VarSize=[1 var_num]; VarMin=-5; VarMax= 5; %PSO Parameters max_epoch=50; ini_pop=25; % Constriction Coefficients phi1=2.1; phi2=2.1; phi=phi1+phi2; khi=2/(phi-2+sqrt(phi^2-4*phi)); w=khi; % Inertia Weight wdamp=0.99; % Inertia Weight Damping Ratio c1=khi*phi1; % Personal Learning Coefficient c2=khi*phi2; % Global Learning Coefficient % Velocity Limits VelMax=0.1*(VarMax-VarMin); VelMin=-VelMax; % Initialization empty_particle.Position=[]; empty_particle.Cost=[]; empty_particle.Velocity=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; particle=repmat(empty_particle,ini_pop,1); GlobalBest.Cost=inf; Cost_Test= zeros(50,1); for i=1:ini_pop % Initialize Position particle(i).Position= WEIGHTS(i ,:); % Initialize Velocity particle(i).Velocity=zeros(VarSize); % Evaluation particle(i).Cost=mape_calc(particle(i).Position,trainset); Cost_Test(i)=mape_calc(particle(i).Position,testset); % Update Personal Best particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; % Update Global Best if particle(i).Best.Cost<GlobalBest.Cost GlobalBest=particle(i).Best; end end BestCost_Train=zeros(max_epoch,1); BestCost_Test=zeros(max_epoch,1); [~, SortOrder]=sort(Cost_Test); Cost_Test =Cost_Test(SortOrder); % PSO Main Loop for it=1:max_epoch for i=1:ini_pop % Update Velocity particle(i).Velocity = w*particle(i).Velocity ... +c1*rand(VarSize).*(particle(i).Best.Position-particle(i).Position) ... +c2*rand(VarSize).*(GlobalBest.Position-particle(i).Position); % Apply Velocity Limits particle(i).Velocity = max(particle(i).Velocity,VelMin); particle(i).Velocity = min(particle(i).Velocity,VelMax); % Update Position particle(i).Position = particle(i).Position + particle(i).Velocity; IsOutside=(particle(i).Position<VarMin | particle(i).Position>VarMax); particle(i).Velocity(IsOutside)=-particle(i).Velocity(IsOutside); % Apply Position Limits particle(i).Position = max(particle(i).Position,VarMin); particle(i).Position = min(particle(i).Position,VarMax); % Evaluation particle(i).Cost = mape_calc(particle(i).Position,trainset); for l= 1:ini_pop Cost_Test(l)=mape_calc(particle(l).Position,testset); end [~, SortOrder]=sort(Cost_Test); Cost_Test =Cost_Test(SortOrder); BestCost_Test(it) = Cost_Test(1); % Update Personal Best if particle(i).Cost<particle(i).Best.Cost particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; % Update Global Best if particle(i).Best.Cost<GlobalBest.Cost GlobalBest=particle(i).Best; end end end BestCost_Train(it)=GlobalBest.Cost; disp(['epochs ' num2str(it) ', cost on train data = ' num2str(BestCost_Train(it)) ', cost on test data = ' num2str(BestCost_Test(it))]); w=w*wdamp; end % Results figure; plot(BestCost_Train,'LineWidth',2); xlabel('epochs'); ylabel('train costs'); grid on; figure; plot(BestCost_Test,'LineWidth',2); xlabel('epochs'); ylabel('test costs'); grid on;