基于GA遗传算法优化RBF神经网络进行数据预测matlab仿真+代码操作视频

clc; clear; close all; warning off; addpath(genpath(pwd)); %遗传算法优化来训练RBF网络权值 %G为进化代数,Size为种群规模,CodeL为参数的二进制编码长度 G = 250; Size = 30; CodeL = 10; %确定每个参数的最大最小值 for i = 1:3 MinX(i) = 0.1*ones(1); MaxX(i) = 3*ones(1); end for i = 4:1:9 MinX(i) = -3*ones(1); MaxX(i) = 3*ones(1); end for i = 10:1:12 MinX(i) = -ones(1); MaxX(i) = ones(1); end %初始化种群 E = round(rand(Size,12*CodeL)); BsJ = 0; %进化开始 for kg = 1:1:G time(kg) = kg for s = 1:1:Size m = E(s,:); %取出其中个体 %把二进制表示的参数转化为实数 for j = 1:1:12 y(j) = 0; mj = m((j-1)*CodeL + 1:1:j*CodeL); for i = 1:1:CodeL y(j) = y(j) + mj(i)*2^(i - 1); end f(s,j) = (MaxX(j) - MinX(j))*y(j)/1023 + MinX(j); end p = f(s,:); [p,BsJ] = fitness(p,BsJ); BsJi(s) = BsJ; %记录每个个体的总误差 end %对误差排序，求出最好误差 [OderJi,IndexJi] = sort(BsJi); BestJ(kg) = OderJi(1); BJ = BestJ(kg); Ji = BsJi + 1e-10; %对误差取倒数，求出适应度值 fi = 1./Ji; %适应度值 [Oderfi,Indexfi] = sort(fi); Bestfi = Oderfi(Size); %最佳适应度值 BestS = E(Indexfi(Size),:); %最佳个体 kg %进化次数 p %最佳个体 BJ %最佳个体的误差 %**************Step 2:选择操作**********************% fi_sum = sum(fi); fi_Size = (Oderfi/fi_sum)*Size; fi_S = floor(fi_Size); kk = 1; for i = 1:1:Size for j = 1:1:fi_S(i) TempE(kk,:) = E(Indexfi(i),:); kk = kk + 1; end end %***************Step 3:交叉操作***********************************% pc = 0.60; n = ceil(20*rand); for i = 1:2:(Size-1) temp = rand; if pc>temp for j = n:1:20 TempE(i,j) = E(i+1,j); TempE(i+1,j) = E(i,j); end end end TempE(Size,:) = BestS; E = TempE; %***************Step 4:变异操作**********************************% pm = 0.001 - [1:1:Size]*(0.001)/Size; for i = 1:1:Size for j = 1:1:12*CodeL temp = rand; if pm>temp if TempE(i,j) == 0 TempE(i,j) = 1; else TempE(i,j) = 0; end end end end %把最佳个体赋于种群中 TempE(Size,:) = BestS; E = TempE; end Bestfi BestS fi Best_J = BestJ(G) figure(1) plot(time,BestJ); title('遗传算法优化RBF网络权值中最小误差进化过程') xlabel('进化次数'); ylabel('最小误差'); save pfile p;