四输入两输出BP神经网络MATLAB怎么编写

在你的代码基础上说了。

clc;clear;

close all;

p=load('originaldatatxt');%你问题最后说的数据文件名跟这个不同。

p1=p';

t=[1];% 这个输出（Targets）应该和输入数据对应，输入数据有10个，输出应该也是10个

所以改为 t是一个1x10的行向量，每个元素对应10个输入数据的输出。不知道你的训练数据的输出是不是都是1？我试了试假设你的数据的输出都是1，所以t=ones(1,10),可以运行。

P=[];‘输入，开盘价，最高价，最低价，收盘价成交量依次5天的数据’

T=[];’输出，即第二日的收盘’

net=newff(minmax(P),[7,1],{'tansig','logsig'},'traingdx');

nettrainParamepochs=1000; ‘最大训练次数，根据需要可自行调节’

nettrainParamgoal=001; ‘误差’

nettrainParamlr=001; ‘学习率’

net=train(net,P,T); ‘训练网络’

test=[];‘待预测数据输入’

out=sim(net,test); ‘仿真预测’

我的这个程序没有进行初始化，你还需要先将数据进行初始化后才能算。

input_train=[你的训练样本数据];

output_train=[1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1];%分成4类

[

net=newff(minmax(h'),[12,4],{'tansig' 'purelin'},'traingdx');

nettrainParamgoal = 0001;

nettrainParamepochs = 10000;

nettrainParamshow=20;

net=train(net,input_train,output_train);

an=sim(net,input_test);

这个问题也困扰了我好久，终于解决了。给你个gam程序，新建m文件复制进去，再运行程序试试。

%gam

function [x,endPop,bPop,traceInfo] = ga(bounds,evalFN,evalOps,startPop,opts,

termFN,termOps,selectFN,selectOps,xOverFNs,xOverOps,mutFNs,mutOps)

% GA run a genetic algorithm

% 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 initializem

% 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) ([008])

% xOverFNS - a string containing blank seperated names of Xoverm

% files (['arithXover heuristicXover simpleXover'])

% xOverOps - A matrix of options to pass to Xoverm 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 mutationm

% files (['boundaryMutation multiNonUnifMutation

% nonUnifMutation unifMutation'])

% mutOps - A matrix of options to pass to Xoverm 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])

% Binary and Real-Valued Simulation Evolution for Matlab

% Copyright (C) 1996 CR Houck, JA Joines, MG Kay

%

% CR Houck, JJoines, and MKay A genetic algorithm for function

% optimization: A Matlab implementation ACM Transactions on Mathmatical

% Software, Submitted 1996

%

% This program is free software; you can redistribute it and/or modify

% it under the terms of the GNU General Public License as published by

% the Free Software Foundation; either version 1, or (at your option)

% any later version

%

% This program is distributed in the hope that it will be useful,

% but WITHOUT ANY WARRANTY; without even the implied warranty of

% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE See the

% GNU General Public License for more details A copy of the GNU

% General Public License can be obtained from the

% Free Software Foundation, Inc, 675 Mass Ave, Cambridge, MA 02139, USA

%%$Log: gam,v $

%Revision 110 1996/02/02 15:03:00 jjoine

% Fixed the ordering of imput arguments in the comments to match

% the actual order in the ga function

%

%Revision 19 1995/08/28 20:01:07 chouck

% Updated initialization parameters, updated mutation parameters to reflect

% b being the third option to the nonuniform mutations

%

%Revision 18 1995/08/10 12:59:49 jjoine

%Started Logfile to keep track of revisions

%

n=nargin;

if n<2 | n==6 | n==10 | n==12

disp('Insufficient arguements')

end

if n<3 %Default evalation opts

evalOps=[];

end

if n<5

opts = [1e-6 1 0];

end

if isempty(opts)

opts = [1e-6 1 0];

end

if any(evalFN<48) %Not using a m file

if opts(2)==1 %Float ga

e1str=['x=c1; c1(xZomeLength)=', evalFN ';'];

e2str=['x=c2; c2(xZomeLength)=', evalFN ';'];

else %Binary ga

e1str=['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=',

evalFN ';'];

end

else %Are using a m file

if opts(2)==1 %Float ga

e1str=['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);'];

e2str=['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);'];

else %Binary ga

e1str=['x=b2f(endPop(j,:),bounds,bits);[x v]=' evalFN

'(x,[gen evalOps]); endPop(j,:)=[f2b(x,bounds,bits) v];'];

end

end

if n<6 %Default termination information

termOps=[100];

termFN='maxGenTerm';

end

if n<12 %Default muatation information

if opts(2)==1 %Float GA

mutFNs=['boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation'];

mutOps=[4 0 0;6 termOps(1) 3;4 termOps(1) 3;4 0 0];

else %Binary GA

mutFNs=['binaryMutation'];

mutOps=[005];

end

end

if n<10 %Default crossover information

if opts(2)==1 %Float GA

xOverFNs=['arithXover heuristicXover simpleXover'];

xOverOps=[2 0;2 3;2 0];

else %Binary GA

xOverFNs=['simpleXover'];

xOverOps=[06];

end

end

if n<9 %Default select opts only ie roullete wheel

selectOps=[];

end

if n<8 %Default select info

selectFN=['normGeomSelect'];

selectOps=[008];

end

if n<6 %Default termination information

termOps=[100];

termFN='maxGenTerm';

end

if n<4 %No starting population passed given

startPop=[];

end

if isempty(startPop) %Generate a population at random

%startPop=zeros(80,size(bounds,1)+1);

startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2));

end

if opts(2)==0 %binary

bits=calcbits(bounds,opts(1));

end

xOverFNs=parse(xOverFNs);

mutFNs=parse(mutFNs);

xZomeLength = size(startPop,2); %Length of the xzome=numVars+fittness

numVar = xZomeLength-1; %Number of variables

popSize = size(startPop,1); %Number of individuals in the pop

endPop = zeros(popSize,xZomeLength); %A secondary population matrix

c1 = zeros(1,xZomeLength); %An individual

c2 = zeros(1,xZomeLength); %An individual

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

while(~done)

%Elitist Model

[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

if ( (abs(bval - oval)>epsilon) | (gen==1)) %If we have a new best sol

if display

fprintf(1,'\n%d %f\n',gen,bval); %Update the display

end

if floatGA

bPop(bFoundIn,:)=[gen startPop(bindx,:)]; %Update bPop Matrix

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

endPop = feval(selectFN,startPop,[gen selectOps]); %Select

if floatGA %Running with the model where the parameters are numbers of ops

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,:)]);

if c1(1:numVar)==endPop(a,(1:numVar)) %Make sure we created a new

c1(xZomeLength)=endPop(a,xZomeLength); %solution before evaluating

elseif c1(1:numVar)==endPop(b,(1:numVar))

c1(xZomeLength)=endPop(b,xZomeLength);

else

%[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);

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

%[c2(xZomeLength) c2] = feval(evalFN,c2,[gen evalOps]);

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

%[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);

eval(e1str);

end

endPop(a,:)=c1;

end

end

else %We are running a probabilistic model of genetic operators

for i=1:numXOvers,

xN=deblank(xOverFNs(i,:)); %Get the name of crossover function

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

gen=gen+1;

done=feval(termFN,[gen termOps],bPop,endPop); %See if the ga is done

startPop=endPop; %Swap the populations

[bval,bindx] = min(startPop(:,xZomeLength)); %Keep the best solution

startPop(bindx,:) = best; %replace it with the worst

end

[bval,bindx] = max(startPop(:,xZomeLength));

if display

fprintf(1,'\n%d %f\n',gen,bval);

end

x=startPop(bindx,:);

if opts(2)==0 %binary

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

if collectTrace

traceInfo(gen,1)=gen; %current generation

traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness

traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittness

end