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 == 12
disp('Insufficient arguements')
end
% 默认评估选项
if n < 3
evalOps = [];
end
% 默认参数
if n < 5
opts = [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) == 1
e1str = ['x=c1; c1(xZomeLength)=', evalFN ';'];
e2str = ['x=c2; c2(xZomeLength)=', evalFN ';'];
% 二进制编码
else
e1str = ['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=', evalFN ';'];
end
else
% 浮点数编码
if opts(2) == 1
e1str = ['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);'];
e2str = ['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);'];
% 二进制编码
else
e1str=['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 < 6
termOps = 100;
termFN = 'maxGenTerm';
end
%% 默认变异信息
if n < 12
% 浮点数编码
if opts(2) == 1
mutFNs = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';
mutOps = [4, 0, 0; 6, termOps(1), 3; 4, termOps(1), 3;4, 0, 0];
% 二进制编码
else
mutFNs = 'binaryMutation';
mutOps = 0.05;
end
end
%% 默认交叉信息
if n < 10
% 浮点数编码
if opts(2) == 1
xOverFNs = 'arithXover heuristicXover simpleXover';
xOverOps = [2, 0; 2, 3; 2, 0];
% 二进制编码
else
xOverFNs = 'simpleXover';
xOverOps = 0.6;
end
end
%% 仅默认选择选项，即轮盘赌。
if n < 9
selectOps = [];
end
%% 默认选择信息
if n < 8
selectFN = 'normGeomSelect';
selectOps = 0.08;
end
%% 默认终止信息
if n < 6
termOps = 100;
termFN = 'maxGenTerm';
end
%% 没有定的初始种群
if n < 4
startPop = [];
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) == 0
bits = 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 pop
best = startPop(bindx, :);
if collectTrace
traceInfo(gen, 1) = gen; % current generation
traceInfo(gen, 2) = startPop(bindx, xZomeLength); % Best fittness
traceInfo(gen, 3) = mean(startPop(:, xZomeLength)); % Avg fittness
traceInfo(gen, 4) = std(startPop(:, xZomeLength));
end
%% 最佳解
if ( (abs(bval - oval) > epsilon) || (gen==1))
% 更新显示
if display
fprintf(1, '\n%d %f\n', gen, bval);
end
% 更新种群矩阵
if floatGA
bPop(bFoundIn, :) = [gen, startPop(bindx, :)];
else
bPop(bFoundIn, :) = [gen, b2f(startPop(bindx, 1 : numVar), bounds, bits)...
startPop(bindx, xZomeLength)];
end
bFoundIn = bFoundIn + 1; % Update number of changes
oval = bval; % Update the best val
else
if display
fprintf(1,'%d ',gen); % Otherwise just update num gen
end
end
%% 选择种群
endPop = feval(selectFN, startPop, [gen, selectOps]);
% 以参数为操作数的模型运行
if floatGA
for i = 1 : numXOvers
for j = 1 : xOverOps(i, 1)
a = round(rand * (popSize - 1) + 1); % Pick a parent
b = round(rand * (popSize - 1) + 1); % Pick another parent
xN = 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);
else
eval(e1str);
end
if 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);
else
eval(e2str);
end
endPop(a, :) = c1;
endPop(b, :) = c2;
end
end
for i = 1 : numMuts
for 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);
else
eval(e1str);
end
endPop(a, :) = c1;
end
end
%% 运行遗传算子的概率模型
else
for i = 1 : numXOvers
xN = 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));
end
cp = 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, :)]);
end
end
for i = 1 : numMuts
mN = deblank(mutFNs(i, :));
for j = 1 : popSize
endPop(j, :) = feval(mN, endPop(j, :), bounds, [gen, mutOps(i, :)]);
eval(e1str);
end
end
end
% 更新记录
gen = gen + 1;
done = feval(termFN, [gen, termOps], bPop, endPop); % See if the ga is done
startPop = endPop; % Swap the populations
[~, bindx] = min(startPop(:, xZomeLength)); % Keep the best solution
startPop(bindx, :) = best; % replace it with the worst
end
[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) == 0
x = b2f(x, bounds,bits);
bPop(bFoundIn, :) = [gen, b2f(startPop(bindx, 1 : numVar), bounds, bits)...
startPop(bindx, xZomeLength)];
else
bPop(bFoundIn, :) = [gen, startPop(bindx, :)];
end
%% 赋值
if collectTrace
traceInfo(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
