Multiple input and multiple output | MATLAB implements GA-BP genetic algorithm to optimize BP neural network multiple input and multiple output
Table of contents
Prediction effect
basic introduction
Multiple input and multiple output | MATLAB implements GA-BP genetic algorithm to optimize BP neural network multiple input and multiple output
1.data is a data set with 10 input features and 3 output variables.
2.main.m is the main program file.
3. The command window outputs MBE, MAE and R2, and the data and program content can be obtained in the download area.
programming
- How to download the complete program and data: privately message the blogger to reply to MATLAB to implement the GA-BP genetic algorithm to optimize the BP neural network with multiple inputs and multiple outputs .
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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
Past highlights
MATLAB implements RBF radial basis neural network multi-input multi-output prediction
MATLAB implements BP neural network multi-input multi-output prediction
MATLAB implements DNN neural network multi-input multi-output prediction
References
[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