【预测模型】基于鲸鱼优化算法优化神经网络实现数据预测附matlab代码（WOA-NN） 上传版本.zip

clc tic close all clear all rng default filename = 'wdbc1.xlsx'; sheetname1 = 'Sheet1'; sheetname2 = 'Sheet2'; gbest = 0.0; input = xlsread(filename,sheetname1,'A1:Z10000'); target = xlsread(filename,sheetname2,'A1:Z10000'); inputs=input'; targets=target'; m=length(inputs(:,1)); o=length(targets(:,1)); n=10; net=feedforwardnet(n); net=configure(net,inputs,targets); kk=m*n+n+n+o; for j=1:kk LB(1,j)=-1.5; UB(1,j)=1.5; end pop=10; for i=1:pop for j=1:kk xx(i,j)=LB(1,j)+rand*(UB(1,j)-LB(1,j)); end end maxrun=1; for run=1:maxrun fun=@(x) myfunc(x,n,m,o,net,inputs,targets); x0=xx; % pso initialization----------------------------------------------start x=x0; % initial population %v=0.1*x0; % initial velocity for i=1:pop f0(i,1)=fun(x0(i,:)); end [fmin0,index0]=min(f0); pbest=x0; % initial pbest gbest=x0(index0,:); % initial gbest % pso initialization------------------------------------------------end % pso algorithm---------------------------------------------------start %c1=1.5; %c2=2.5; ite=1; maxite=100; tolerance=1; while ite<=maxite %&& tolerance>10^-8 %w=0.1+rand*0.4; % pso velocity updates A1=2-ite*((2)/maxite); A2=-1+ite*((-1)/maxite); % for i=1:pop % for j=1:kk % v(i,j)=w*v(i,j)+c1*rand*(pbest(i,j)-x(i,j))... % +c2*rand*(gbest(1,j)-x(i,j)); % end % end % pso position update for i=1:pop r1=rand(); % r1 is a random number in [0,1] r2=rand(); % r2 is a random number in [0,1] A=2*A1*r1-A1; % Eq. (2.3) in the paper C=2*r2; % Eq. (2.4) in the paper b=1; % parameters in Eq. (2.5) l=(A2-1)*rand+1; % parameters in Eq. (2.5) p = rand(); % p in Eq. (2.6) for j=1:kk if p<0.5 if abs(A)>=1 rand_leader_index = floor(pop*rand()+1); X_rand = x(rand_leader_index, :); D_X_rand=abs(C*X_rand(j)-x(i,j)); % Eq. (2.7) x(i,j)=X_rand(j)-A*D_X_rand; % Eq. (2.8) elseif abs(A)<1 D_Leader=abs(C*pbest(j)-x(i,j)); % Eq. (2.1) x(i,j)=pbest(j)-A*D_Leader; % Eq. (2.2) end elseif p>=0.5 distance2Leader=abs(pbest(j)-x(i,j)); % Eq. (2.5) x(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+pbest(j); end % x(i,j)=x(i,j)+v(i,j); end end % handling boundary violations for i=1:pop for j=1:kk if x(i,j)<LB(j) x(i,j)=LB(j); elseif x(i,j)>UB(j) x(i,j)=UB(j); end end end % evaluating fitness for i=1:pop f(i,1)=fun(x(i,:)); end % updating pbest and fitness for i=1:pop if f(i,1)<f0(i,1) pbest(i,:)=x(i,:); f0(i,1)=f(i,1); end end [fmin,index]=min(f0); % finding out the best particle ffmin(ite,run)=fmin; % storing best fitness ffite(run)=ite; % storing iteration count % updating gbest and best fitness if fmin<fmin0 gbest=pbest(index,:); fmin0=fmin; end % calculating tolerance % if ite>100; % tolerance=abs(ffmin(ite-100,run)-fmin0); % end % displaying iterative results if ite==1 disp(sprintf('Iteration Best particle Objective fun')); end disp(sprintf('%8g %8g %8.4f',ite,index,fmin0)); ite=ite+1; end % pso algorithm-----------------------------------------------------end xo=gbest; fval=fun(xo); xbest(run,:)=xo; ybest(run,1)=fun(xo); disp(sprintf('****************************************')); disp(sprintf(' RUN fval ObFuVa')); disp(sprintf('%6g %6g %8.4f %8.4f',run,fval,ybest(run,1))); end toc % Final neural network model disp('Final nn model is net_f') net_f = feedforwardnet(n); net_f=configure(net_f,inputs,targets); [a b]=min(ybest); xo=xbest(b,:); k=0; for i=1:n for j=1:m k=k+1; xi(i,j)=xo(k); end end for i=1:n k=k+1; xl(i)=xo(k); xb1(i,1)=xo(k+n); end for i=1:o k=k+1; xb2(i,1)=xo(k); end net_f.iw{1,1}=xi; net_f.lw{2,1}=xl; net_f.b{1,1}=xb1; net_f.b{2,1}=xb2; %Calculation of MSE err = sum((net_f(inputs)-targets).^2)/length(net_f(inputs)) %Regression plot scatter(targets,net_f(inputs)) disp('Trained ANN net_f is ready for the use'); %Trained ANN net_f is ready for the use plot(ybest(run,1));