用遗传算法优化BP神经网络，在加粗那行出现输入参数不足，是什么原因啊，求大神指导，代码如下
function err=Bpfun(x,P,T,hiddennum,P_test,T_test)
fid=fopen(‘xlsr.txt’,‘rt’);
P=fscanf(fid,’%f’, [15,9]);
status=fclose(fid);

fid=fopen(‘xlsc.txt’,‘rt’);
T=fscanf(fid,’%f’,[3,9]);
status=fclose(fid);

fid=fopen(‘cssr.txt’,‘rt’);
P_test=fscanf(fid,’%f’, [15,3]);
status=fclose(fid);

fid=fopen(‘cssc.txt’,‘rt’);
T_test=fscanf(fid,’%f’, [3,3]);
status=fclose(fid);
hiddennum=31;
inputnum=size(P,1);
outputnum=size(T,1);
net=newff(minmax§,[hiddennum,outputnum],{‘tansig’,‘logsig’},‘trainlm’);
net.trainParam.epochs=1000;
net.trainParam.goal=0.01;
net.trainParam.lr=0.1;
net.trainParam.show=NaN;
%net.trainParam.showwindow=false;
w1num=inputnumhiddennum;
w2num=outputnumhiddennum;
w1=x(1:w1num);
B1=x(w1num+1:w1num+hiddennum);
w2=x(w1num+hiddennum+1:w1num+hiddennum+w2num);
B2=x(w1num+hiddennum+w2num+1:w1num+hiddennum+w2num+outputnum);
net.iw{1,1}=reshape(w1,hiddennum,inputnum);
net.lw{2,1}=reshape(w2,outputnum,hiddennum);
net.b{1}=reshape(B1,hiddennum,1);
net.b{2}=reshape(B2,outputnum,1);
net=train(net,P,T);
Y=sim(net,P_test);
err=norm(Y-T_test);

clc
clear all
close all
load data
hiddennum=31;
threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1];
inputnum=size(P,1);
outputnum=size(T,1);
w1num=inputnumhiddennum;
w2num=outputnumhiddennum;
N=w1num+hiddennum+w2num+outputnum;
NIND=40;
MAXGEN=50;
PRECI=10;
GGAP=0.95;
px=0.7;
pm=0.01;
trace=zeros(N+1,MAXGEN);

FieldD=[repmat(PRECI,1,N);repmat([-0.5;0.5],1,N);repmat([1;0;1;1],1,N)];
Chrom=crtbp(NIND,PRECI*N);
gen=0;
X=bs2rv(Chrom,FieldD);
ObjV=Objfun(X,P,T,hiddennum,P_test,T_test);
while gen<MAXGEN
fprintf(’%d\n’,gen)
FitnV=ranking(ObjV);
SelCh=select(‘sus’,Chrom,FitnV,GGAP);
SelCh=recombin(‘xovsp’,SelCh,px);
SelCh=mut(SelCh,pm);
X=bs2rv(SelCh,FieldD);
ObjVSel=Objfun(X,P,T,hiddennum,P_test,T_test);
[Chrom,ObjV]=reins(Chrom,SelCh,1,1,ObjV,ObjVSel);
X=bs2rv(Chrom,FieldD);
gen=gen+1;
[Y,I]=min(ObjV);
trace(1:N,gen)=X(I,:);
trace(end,gen)=Y;
end

figure(1);
plot(1:MAXGEN,trace(end,:));
grid on
xlabel(‘遗传代数’)
ylabel(‘误差的变化’)
title(‘进化过程’)
bestX=trace(1:end-1,end);
bestErr=trace(end,end);
fprintf([‘最优初始权值和阈值:\nX=’,num2str(bestX’),’\n最小误差err=’,num2str(bestErr),’\n’])

function Obj=Objfun(X,P,T,hiddennum,P_test,T_test)
[M,~]=size(X);
Obj=zeros(M,1);
for i=1:M
Obj(i)=BPfun(X(i,:),P,T,hiddennum,P_test,T_test);
end