免费简历制作网站推荐,广西壮族自治区图书馆,wordpress商城 插件,企业网设计案例分析多输入多输出 | MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出 目录 多输入多输出 | MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出预测效果基本介绍程序设计往期精彩参考资料 预测效果 基本介绍 多输入多输出 | MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出…多输入多输出 | MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出 目录 多输入多输出 | MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出预测效果基本介绍程序设计往期精彩参考资料 预测效果 基本介绍 多输入多输出 | MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出 1.data为数据集10个输入特征3个输出变量。 2.main.m为主程序文件。 3.命令窗口输出MBE、MAE和R2可在下载区获取数据和程序内容。 程序设计
完整程序和数据下载方式私信博主回复MATLAB实现GA-BP遗传算法优化BP神经网络多输入多输出。
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
function [x, endPop, bPop, traceInfo] ga(bounds, evalFN, evalOps, startPop, opts, ...
termFN, termOps, selectFN, selectOps, xOverFNs, xOverOps, mutFNs, mutOps)% Output Arguments:
% x - the best solution found during the course of the run
% endPop - the final population
% bPop - a trace of the best population
% traceInfo - a matrix of best and means of the ga for each generation
%
% Input Arguments:
% bounds - a matrix of upper and lower bounds on the variables
% evalFN - the name of the evaluation .m function
% evalOps - options to pass to the evaluation function ([NULL])
% startPop - a matrix of solutions that can be initialized
% from initialize.m
% opts - [epsilon prob_ops display] change required to consider two
% solutions different, prob_ops 0 if you want to apply the
% genetic operators probabilisticly to each solution, 1 if
% you are supplying a deterministic number of operator
% applications and display is 1 to output progress 0 for
% quiet. ([1e-6 1 0])
% termFN - name of the .m termination function ([maxGenTerm])
% termOps - options string to be passed to the termination function
% ([100]).
% selectFN - name of the .m selection function ([normGeomSelect])
% selectOpts - options string to be passed to select after
% select(pop,#,opts) ([0.08])
% xOverFNS - a string containing blank seperated names of Xover.m
% files ([arithXover heuristicXover simpleXover])
% xOverOps - A matrix of options to pass to Xover.m files with the
% first column being the number of that xOver to perform
% similiarly for mutation ([2 0;2 3;2 0])
% mutFNs - a string containing blank seperated names of mutation.m
% files ([boundaryMutation multiNonUnifMutation ...
% nonUnifMutation unifMutation])
% mutOps - A matrix of options to pass to Xover.m files with the
% first column being the number of that xOver to perform
% similiarly for mutation ([4 0 0;6 100 3;4 100 3;4 0 0])%% 初始化参数
n nargin;
if n 2 || n 6 || n 10 || n 12disp(Insufficient arguements)
end% 默认评估选项
if n 3 evalOps [];
end% 默认参数
if n 5opts [1e-6, 1, 0];
end% 默认参数
if isempty(opts)opts [1e-6, 1, 0];
end%% 判断是否为m文件
if any(evalFN 48)% 浮点数编码 if opts(2) 1e1str [xc1; c1(xZomeLength), evalFN ;]; e2str [xc2; c2(xZomeLength), evalFN ;]; % 二进制编码elsee1str [xb2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength), evalFN ;];end
else% 浮点数编码if opts(2) 1e1str [[c1 c1(xZomeLength)] evalFN (c1,[gen evalOps]);]; e2str [[c2 c2(xZomeLength)] evalFN (c2,[gen evalOps]);];% 二进制编码elsee1str[xb2f(endPop(j,:),bounds,bits);[x v] evalFN ...(x,[gen evalOps]); endPop(j,:)[f2b(x,bounds,bits) v];]; end
end%% 默认终止信息
if n 6termOps 100;termFN maxGenTerm;
end%% 默认变异信息
if n 12% 浮点数编码if opts(2) 1mutFNs boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation;mutOps [4, 0, 0; 6, termOps(1), 3; 4, termOps(1), 3;4, 0, 0];% 二进制编码elsemutFNs binaryMutation;mutOps 0.05;end
end%% 默认交叉信息
if n 10% 浮点数编码if opts(2) 1xOverFNs arithXover heuristicXover simpleXover;xOverOps [2, 0; 2, 3; 2, 0];% 二进制编码elsexOverFNs simpleXover;xOverOps 0.6;end
end%% 仅默认选择选项即轮盘赌。
if n 9selectOps [];
end%% 默认选择信息
if n 8selectFN normGeomSelect;selectOps 0.08;
end%% 默认终止信息
if n 6termOps 100;termFN maxGenTerm;
end%% 没有定的初始种群
if n 4startPop [];
end%% 随机生成种群
if isempty(startPop)startPop initializega(80, bounds, evalFN, evalOps, opts(1: 2));
end%% 二进制编码
if opts(2) 0bits calcbits(bounds, opts(1));
end%% 参数设置
xOverFNs parse(xOverFNs);
mutFNs parse(mutFNs);
xZomeLength size(startPop, 2); % xzome 的长度
numVar xZomeLength - 1; % 变量数
popSize size(startPop,1); % 种群人口个数
endPop zeros(popSize, xZomeLength); % 第二种群矩阵
numXOvers size(xOverFNs, 1); % Number of Crossover operators
numMuts size(mutFNs, 1); % Number of Mutation operators
epsilon opts(1); % Threshold for two fittness to differ
oval max(startPop(:, xZomeLength)); % Best value in start pop
bFoundIn 1; % Number of times best has changed
done 0; % Done with simulated evolution
gen 1; % Current Generation Number
collectTrace (nargout 3); % Should we collect info every gen
floatGA opts(2) 1; % Probabilistic application of ops
display opts(3); % Display progress %% 精英模型
while(~done)[bval, bindx] max(startPop(:, xZomeLength)); % Best of current popbest startPop(bindx, :);if collectTracetraceInfo(gen, 1) gen; % current generationtraceInfo(gen, 2) startPop(bindx, xZomeLength); % Best fittnesstraceInfo(gen, 3) mean(startPop(:, xZomeLength)); % Avg fittnesstraceInfo(gen, 4) std(startPop(:, xZomeLength)); end%% 最佳解if ( (abs(bval - oval) epsilon) || (gen1))% 更新显示if displayfprintf(1, \n%d %f\n, gen, bval); end% 更新种群矩阵if floatGAbPop(bFoundIn, :) [gen, startPop(bindx, :)]; elsebPop(bFoundIn, :) [gen, b2f(startPop(bindx, 1 : numVar), bounds, bits)...startPop(bindx, xZomeLength)];endbFoundIn bFoundIn 1; % Update number of changesoval bval; % Update the best valelseif displayfprintf(1,%d ,gen); % Otherwise just update num genendend
%% 选择种群endPop feval(selectFN, startPop, [gen, selectOps]);% 以参数为操作数的模型运行if floatGAfor i 1 : numXOversfor j 1 : xOverOps(i, 1)a round(rand * (popSize - 1) 1); % Pick a parentb round(rand * (popSize - 1) 1); % Pick another parentxN deblank(xOverFNs(i, :)); % Get the name of crossover function[c1, c2] feval(xN, endPop(a, :), endPop(b, :), bounds, [gen, xOverOps(i, :)]);% Make sure we created a new if c1(1 : numVar) endPop(a, (1 : numVar)) c1(xZomeLength) endPop(a, xZomeLength);elseif c1(1:numVar) endPop(b, (1 : numVar))c1(xZomeLength) endPop(b, xZomeLength);elseeval(e1str);endif c2(1 : numVar) endPop(a, (1 : numVar))c2(xZomeLength) endPop(a, xZomeLength);elseif c2(1 : numVar) endPop(b, (1 : numVar))c2(xZomeLength) endPop(b, xZomeLength);elseeval(e2str);endendPop(a, :) c1;endPop(b, :) c2;endendfor i 1 : numMutsfor j 1 : mutOps(i, 1)a round(rand * (popSize - 1) 1);c1 feval(deblank(mutFNs(i, :)), endPop(a, :), bounds, [gen, mutOps(i, :)]);if c1(1 : numVar) endPop(a, (1 : numVar)) c1(xZomeLength) endPop(a, xZomeLength);elseeval(e1str);endendPop(a, :) c1;endend%% 运行遗传算子的概率模型else for i 1 : numXOversxN deblank(xOverFNs(i, :));cp find((rand(popSize, 1) xOverOps(i, 1)) 1);if rem(size(cp, 1), 2) cp cp(1 : (size(cp, 1) - 1)); endcp reshape(cp, size(cp, 1) / 2, 2);for j 1 : size(cp, 1)a cp(j, 1); b cp(j, 2); [endPop(a, :), endPop(b, :)] feval(xN, endPop(a, :), endPop(b, :), ...bounds, [gen, xOverOps(i, :)]);endendfor i 1 : numMutsmN deblank(mutFNs(i, :));for j 1 : popSizeendPop(j, :) feval(mN, endPop(j, :), bounds, [gen, mutOps(i, :)]);eval(e1str);endendend% 更新记录gen gen 1;done feval(termFN, [gen, termOps], bPop, endPop); % See if the ga is donestartPop endPop; % Swap the populations[~, bindx] min(startPop(:, xZomeLength)); % Keep the best solutionstartPop(bindx, :) best; % replace it with the worstend
[bval, bindx] max(startPop(:, xZomeLength));%% 显示结果
if display fprintf(1, \n%d %f\n, gen, bval);
end%% 二进制编码
x startPop(bindx, :);
if opts(2) 0x b2f(x, bounds,bits);bPop(bFoundIn, :) [gen, b2f(startPop(bindx, 1 : numVar), bounds, bits)...startPop(bindx, xZomeLength)];
elsebPop(bFoundIn, :) [gen, startPop(bindx, :)];
end%% 赋值
if collectTracetraceInfo(gen, 1) gen; % 当前迭代次数traceInfo(gen, 2) startPop(bindx, xZomeLength); % 最佳适应度traceInfo(gen, 3) mean(startPop(:, xZomeLength)); % 平均适应度
end
往期精彩 MATLAB实现RBF径向基神经网络多输入多输出预测 MATLAB实现BP神经网络多输入多输出预测 MATLAB实现DNN神经网络多输入多输出预测 参考资料 [1] https://blog.csdn.net/kjm13182345320/article/details/116377961 [2] https://blog.csdn.net/kjm13182345320/article/details/127931217 [3] https://blog.csdn.net/kjm13182345320/article/details/127894261
