FA-BP萤火虫算法优化BP神经网络多输入单输出回归预测（Matlab完整程序和数据）

%% this code for regression based on fa-bp %% initiate clc clear close all tic % load data input=xlsread('16组数据','B2:F17'); output=xlsread('16组数据','G2:G17'); %% normlization [inputn,inputps] = mapminmax(input',0,1); [outputn,outputps]=mapminmax(output',0,1); Pn_train=inputn; Tn_train=outputn; Pn_test=inputn; T_test=outputn; % number of node inputnum=size(Pn_train,1); hiddennum=3; outputnum=1; net=newff(Pn_train,Tn_train,hiddennum); net.trainParam.epochs=200; net.trainParam.lr=0.1; net.trainParam.goal=0.00001; [net,per2]=train(net,Pn_train,Tn_train); an=sim(net,Pn_test); test_simu=mapminmax('reverse',an,outputps); figure plot(test_simu);hold on;plot(output);legend('预测数据','期望数据');title('优化前') %% d=inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum;% Lb=zeros(1,d); % lower Ub=ones(1,d); % upper u0=Lb+(Ub-Lb).*rand(1,d); para=[20 50 0.25 0.20 1]; % n=para(1); % number of fireflies % MaxGeneration=para(2); %number of pseudo time steps % ------------------------------------------------ % alpha=para(3); % Randomness 0--1 (highly random) % betamn=para(4); % minimum value of beta % gamma=para(5); % Absorption coefficient [nbest,trace]=ffa_mincon(u0,Lb,Ub,para,inputnum,hiddennum,outputnum,net,inputn,outputn); figure plot(trace) xlabel('迭代次数') ylabel('适应度值') title('萤火虫优化BP适应度曲线') x=nbest; %% w1=x(1:inputnum*hiddennum); B1=x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum); w2=x(inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum); B2=x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum); net.iw{1,1}=reshape(w1,hiddennum,inputnum); net.lw{2,1}=reshape(w2,outputnum,hiddennum); net.b{1}=reshape(B1,hiddennum,1); net.b{2}=B2; net.trainParam.epochs=200; net.trainParam.lr=0.1; net.trainParam.goal=0.00001; [net,per2]=train(net,Pn_train,Tn_train); % 反归一化 an=sim(net,Pn_test); test_simu=mapminmax('reverse',an,outputps); error=test_simu-output; E=mean(abs(error./output)) figure plot(test_simu,'b-*') hold on; plot(output,'-o') title('萤火虫优化bp后的结果','fontsize',12) legend('预测数据','期望数据') xlabel('样本编号') ylabel('比较') toc