使用遗传算法和粒子群算法实现训练ANFIS

% % Copyright (c) 2015, Yarpiz (www.yarpiz.com) % All rights reserved. Please read the "license.txt" for license terms. % % Project Code: YPFZ104 % Project Title: Evolutionary ANFIS Traing in MATLAB % Publisher: Yarpiz (www.yarpiz.com) % % Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team) % % Contact Info: sm.kalami@gmail.com, info@yarpiz.com % function bestfis=TrainAnfisUsingGA(fis,data) %% Problem Definition p0=GetFISParams(fis); Problem.CostFunction=@(x) TrainFISCost(x,fis,data); Problem.nVar=numel(p0); Problem.VarMin=-25; Problem.VarMax=25; %% GA Params Params.MaxIt=1000; Params.nPop=25; %% Run GA results=RunGA(Problem,Params); %% Get Results p=results.BestSol.Position.*p0; bestfis=SetFISParams(fis,p); end function results=RunGA(Problem,Params) disp('Starting GA ...'); %% Problem Definition CostFunction=Problem.CostFunction; % Cost Function nVar=Problem.nVar; % Number of Decision Variables VarSize=[1 nVar]; % Size of Decision Variables Matrix VarMin=Problem.VarMin; % Lower Bound of Variables VarMax=Problem.VarMax; % Upper Bound of Variables %% GA Parameters MaxIt=Params.MaxIt; % Maximum Number of Iterations nPop=Params.nPop; % Population Size pc=0.4; % Crossover Percentage nc=2*round(pc*nPop/2); % Number of Offsprings (Parnets) pm=0.7; % Mutation Percentage nm=round(pm*nPop); % Number of Mutants gamma=0.7; mu=0.15; % Mutation Rate beta=8; % Selection Pressure %% Initialization empty_individual.Position=[]; empty_individual.Cost=[]; pop=repmat(empty_individual,nPop,1); for i=1:nPop % Initialize Position if i>1 pop(i).Position=unifrnd(VarMin,VarMax,VarSize); else pop(i).Position=ones(VarSize); end % Evaluation pop(i).Cost=CostFunction(pop(i).Position); end % Sort Population Costs=[pop.Cost]; [Costs, SortOrder]=sort(Costs); pop=pop(SortOrder); % Store Best Solution BestSol=pop(1); % Array to Hold Best Cost Values BestCost=zeros(MaxIt,1); % Store Cost WorstCost=pop(end).Cost; %% Main Loop for it=1:MaxIt P=exp(-beta*Costs/WorstCost); P=P/sum(P); % Crossover popc=repmat(empty_individual,nc/2,2); for k=1:nc/2 % Select Parents Indices i1=RouletteWheelSelection(P); i2=RouletteWheelSelection(P); % Select Parents p1=pop(i1); p2=pop(i2); % Apply Crossover [popc(k,1).Position, popc(k,2).Position]=... Crossover(p1.Position,p2.Position,gamma,VarMin,VarMax); % Evaluate Offsprings popc(k,1).Cost=CostFunction(popc(k,1).Position); popc(k,2).Cost=CostFunction(popc(k,2).Position); end popc=popc(:); % Mutation popm=repmat(empty_individual,nm,1); for k=1:nm % Select Parent i=randi([1 nPop]); p=pop(i); % Apply Mutation popm(k).Position=Mutate(p.Position,mu,VarMin,VarMax); % Evaluate Mutant popm(k).Cost=CostFunction(popm(k).Position); end % Create Merged Population pop=[pop popc popm]; %#ok % Sort Population Costs=[pop.Cost]; [Costs, SortOrder]=sort(Costs); pop=pop(SortOrder); % Update Worst Cost WorstCost=max(WorstCost,pop(end).Cost); % Truncation pop=pop(1:nPop); Costs=Costs(1:nPop); % Store Best Solution Ever Found BestSol=pop(1); % Store Best Cost Ever Found BestCost(it)=BestSol.Cost; % Show Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); end disp('End of GA.'); disp(' '); %% Results results.BestSol=BestSol; results.BestCost=BestCost; end function [y1, y2]=Crossover(x1,x2,gamma,VarMin,VarMax) alpha=unifrnd(-gamma,1+gamma,size(x1)); y1=alpha.*x1+(1-alpha).*x2; y2=alpha.*x2+(1-alpha).*x1; y1=max(y1,VarMin); y1=min(y1,VarMax); y2=max(y2,VarMin); y2=min(y2,VarMax); end function y=Mutate(x,mu,VarMin,VarMax) nVar=numel(x); nmu=ceil(mu*nVar); j=randsample(nVar,nmu)'; sigma=0.1*(VarMax-VarMin); y=x; y(j)=x(j)+sigma*randn(size(j)); y=max(y,VarMin); y=min(y,VarMax); end