运行遗基于遗传算法的BP神经网络MATLAB代码程序时总是出错！！！？？？ 用于数据预测的基于遗传算法BP神经网络的matlab程序
Matlab\u95ee\u9898\uff0d\uff0d\u5982\u4f55\u7528\u9057\u4f20\u7b97\u6cd5\u4f18\u5316BP\u795e\u7ecf\u7f51\u7edc\uff1f\u8fd9\u7bc7\u6587\u732e\uff08\u4e2d\u6587\uff09\u662f\u5982\u4f55\u505a\u7684\uff1f\u6211\u4e0a\u6b21\u53d1\u7ed9\u4f60\u7684\u7a0b\u5e8f\uff0c\u53ea\u8981\u4f60\u4ece\u7f51\u4e0a\u4e0b\u4e00\u4e2amatcom45\u5c31\u884c\u4e86\uff0c\u76f4\u63a5\u88c5\u5728c\u76d8\u5c31\u53ef\u4ee5\u4e86\uff0c\u4f60\u53d1\u7ed9\u6211\u7684\u8bba\u6587\u53d8\u91cf\u592a\u591a\u7528\u4e00\u822c\u7684\u9057\u4f20\u7b97\u6cd5\u4e0d\u884c\uff0c\u6211\u4ece\u7f51\u4e0a\u53d1\u73b0\u4e86\u4e00\u4e2aPID\u795e\u7ecf\u7f51\u7edc\uff0c\u76f8\u5f53\u597d\u7528\uff0c\u4e0d\u7528\u8ba1\u7b97\u9690\u5c42\u6570\u76ee\uff0c\u5f88\u9002\u5408\u7528\u9057\u4f20\u7b97\u6cd5\u8fdb\u884c\u4f18\u5316\uff0c\u6211\u7f16\u4e86\u4e00\u4e2a\u4f8b\u7a0b\u56de\u6765\u53d1\u7ed9\u4f60\u3002
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\u4e3a\u4ec0\u4e48\u4e00\u5b9a\u8981\u7528matlab\uff0c\u7528C++\u81ea\u5df1\u7f16\u5199\u4e0d\u4e5f\u5f88\u597d\u5417\uff1f
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这个问题也困扰了我好久，终于解决了。给你个ga.m程序，新建m文件复制进去，再运行程序试试。%ga.m
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 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])
% Binary and Real-Valued Simulation Evolution for Matlab
% Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay
%
% C.R. Houck, J.Joines, and M.Kay. 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: ga.m,v $
%Revision 1.10 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 1.9 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 1.8 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=[0.05];
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=[0.6];
end
end
if n<9 %Default select opts only i.e. roullete wheel.
selectOps=[];
end
if n<8 %Default select info
selectFN=['normGeomSelect'];
selectOps=[0.08];
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
我运行了一下没出现你说的错误，你换个版本试一下吧，估计是软件的事。
请确认自己安装了goat（下载-解压-File-set path-add with subfolders-save(别忘了)-close）。
按照错误提示来说是系统无法找到nitializega等函数，说明没有将函数所在目录添加进path
将gaot文件夹中的ga.m拷贝到工作目录下即可
我也在弄这个，这个程序是从问路上弄得吧，有答案了给我说声
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绛旓細寤虹珛BP缃戠粶 net=newff(minmax(pp),[15,1],{'logsig','purelin'},'trainlm');搴旂敤閬椾紶绠楁硶瀵逛紭鍖栫綉缁滃垵濮嬪 in=size(pn,1);out=size(tn,1);hi=15;%闅愬惈灞傝妭鐐规暟 L=in*hi+hi*out+hi+out;%閬椾紶绠楁硶缂栫爜闀垮害 aa=ones(L,1)*[-1,1];popu=50;%绉嶇兢瑙勬ā initPpp=initializega(popu,aa,...
绛旓細瀹為獙缁撴灉琛ㄦ槑,鎶婅繖绉鍩轰簬閬椾紶绠楁硶鐨凚P绁炵粡缃戠粶搴旂敤浜庨仴鎰熷奖鍍忕洃鐫ｅ垎绫,鍏锋湁杈冮珮鐨勫垎绫荤簿搴︺ 鍏抽敭璇:BP绁炵粡缃戠粶;閬椾紶绠楁硶;閬ユ劅褰卞儚鍒嗙被 1 寮曡█ 闅忕潃閬ユ劅鎶鏈殑蹇熷彂灞,閬ユ劅鎶鏈凡缁忓箍娉涘簲鐢ㄤ簬鍚勪釜棰嗗煙銆傚叾涓,閬ユ劅褰卞儚鍒嗙被鏄叾閲嶈缁勬垚閮ㄥ垎銆傝繎骞存潵,闅忕潃浜哄伐绁炵粡缃戠粶鐞嗚鐨勫揩閫熷彂灞,绁炵粡缃戠粶鎶鏈棩鐩婃垚涓洪仴鎰熷奖鍍忓垎绫讳腑鐨...
绛旓細杩欏洓涓兘灞炰簬浜哄伐鏅鸿兘绠楁硶鐨鑼冪暣銆傚叾涓BP绠楁硶銆BP绁炵粡缃戠粶鍜岀缁忕綉缁 灞炰簬绁炵粡缃戠粶杩欎釜澶х被銆閬椾紶绠楁硶涓鸿繘鍖栫畻娉曡繖涓ぇ绫汇傜缁忕綉缁滄ā鎷熶汉绫诲ぇ鑴戠缁忚绠楄繃绋嬶紝鍙互瀹炵幇楂樺害闈炵嚎鎬х殑棰勬祴鍜岃绠楋紝涓昏鐢ㄤ簬闈炵嚎鎬ф嫙鍚堬紝璇嗗埆锛岀壒鐐规槸闇瑕佲滆缁冣濓紝缁欎竴浜涜緭鍏ワ紝鍛婅瘔浠栨纭殑杈撳嚭銆傝嫢骞叉鍚庯紝鍐嶇粰鏂扮殑杈撳叆锛岀缁...
