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！！！？？？
matlab运行错误提示：
??? Error using ==> eval
Undefined function or method 'gabpEval' for input arguments of type 'double'.
Error in ==> initializega at 80
eval(estr);
Error in ==> GABPNET at 22
initPpp=initializega(popu,aa,'gabpEval');%初始化种群
下面是程序：
function net=GABPNET(XX,YY)
% 使用遗传算法对BP网络权值阈值进行优化，再用BP算法训练网络
%数据归一化预处理
nntwarn off
XX=[1:19;2:20;3:21;4:22]';
YY=[1:4];
XX=premnmx(XX);
YY=premnmx(YY);
YY;
%创建网络
net=newff(minmax(XX),[19,25,1],{'tansig','tansig','purelin'},'trainlm');
%下面使用遗传算法对网络进行优化
P=XX;
T=YY;
R=size(P,1);
S2=size(T,1);
S1=25;%隐含层节点数
S=R*S1+S1*S2+S1+S2;%遗传算法编码长度
aa=ones(S,1)*[-1,1];
popu=50;%种群规模
save data2 XX YY % 是将 xx,yy 二个变数的数值存入 data2 这个MAT-file，
initPpp=initializega(popu,aa,'gabpEval');%初始化种群
gen=100;%遗传代数
%下面调用gaot工具箱，其中目标函数定义为gabpEval
[x,endPop,bPop,trace]=ga(aa,'gabpEval',[],initPpp,[1e-6 1 1],'maxGenTerm',gen,...
'normGeomSelect',[0.09],['arithXover'],[2],'nonUnifMutation',[2 gen 3]);
%绘收敛曲线图
figure(1)
plot(trace(:,1),1./trace(:,3),'r-');
hold on
plot(trace(:,1),1./trace(:,2),'b-');
xlabel('Generation');
ylabel('Sum-Squared Error');
figure(2)
plot(trace(:,1),trace(:,3),'r-');
hold on
plot(trace(:,1),trace(:,2),'b-');
xlabel('Generation');
ylabel('Fittness');
%下面将初步得到的权值矩阵赋给尚未开始训练的BP网络
[W1,B1,W2,B2,P,T,A1,A2,SE,val]=gadecod(x);
net.LW{2,1}=W1;
net.LW{3,2}=W2;
net.b{2,1}=B1;
net.b{3,1}=B2;
XX=P;
YY=T;
%设置训练参数
net.trainParam.show=1;
net.trainParam.lr=1;
net.trainParam.epochs=50;
net.trainParam.goal=0.001;
%训练网络
net=train(net,XX,YY);
a=sim(net,XX) ;
%程序二：适应值函数
function [sol, val] = gabpEval(sol,options)
% val - the fittness of this individual
% sol - the individual, returned to allow for Lamarckian evolution
% options - [current_generation]
load data2
nntwarn off
XX=premnmx(XX);
YY=premnmx(YY);
P=XX;
T=YY;
R=size(P,1);
S2=size(T,1);
S1=25;%隐含层节点数
S=R*S1+S1*S2+S1+S2;%遗传算法编码长度
for i=1:S,
x(i)=sol(i);
end;
[W1, B1, W2, B2, P, T, A1, A2, SE, val]=gadecod(x);
%程序三：编解码函数
function [W1, B1, W2, B2, P, T, A1, A2, SE, val]=gadecod(x)
load data2
nntwarn off
XX=premnmx(XX);
YY=premnmx(YY);
P=XX;
T=YY;
R=size(P,1);
S2=size(T,1);
S1=25;%隐含层节点数
S=R*S1+S1*S2+S1+S2;%遗传算法编码长度
% 前R*S1个编码为W1
for i=1:S1,
for k=1:R,
W1(i,k)=x(R*(i-1)+k);
end
end
% 接着的S1*S2个编码（即第R*S1个后的编码）为W2
for i=1:S2,
for k=1:S1,
W2(i,k)=x(S1*(i-1)+k+R*S1);
end
end
% 接着的S1个编码（即第R*S1+S1*S2个后的编码）为B1
for i=1:S1,
B1(i,1)=x((R*S1+S1*S2)+i);
end
% 接着的S2个编码（即第R*S1+S1*S2+S1个后的编码）为B2
for i=1:S2,
B2(i,1)=x((R*S1+S1*S2+S1)+i);
end
% 计算S1与S2层的输出
A1=tansig(W1*P,B1);
A2=purelin(W2*A1,B2);
% 计算误差平方和
SE=sumsqr(T-A2);
val=1/SE; % 遗传算法的适应值
为什么预测不准？训练完后把原数据XX代入，结果跟实际的YY相差不小，有时候还很不靠谱。。。求高手。。。
%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。
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本回答被网友采纳
试了，不行。。。我用的2008a版本