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

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 = ['x=c1; c1(xZomeLength)=', evalFN ';'];  e2str = ['x=c2; c2(xZomeLength)=', evalFN ';']; % 二进制编码elsee1str = ['x=b2f(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=['x=b2f(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) || (gen==1))% 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

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MATLAB实现BP神经网络多输入多输出预测
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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