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谢谢 ilikenba 的回复，我是新手，不太明白，研究了半天，编了一点东西，可运行出了问题，帮忙看看是怎么回事，如何修改，谢谢！ p=[ 40 21 2.5 6. 5
! Z; j) _0 k+ ^- m! ]) H+ N$ K 40 25 3. 6.5 10 & j+ {. q( J) ]! B+ Y
40 30 3.5 7. 20
# \/ J7 v3 Z- X+ v0 g) h, f 45 21 2.5 6.5 10 " e9 X. v( d& w, [8 g) T' Y
45 25 3. 7. 20 % @ [/ \2 `/ f" g& C( w# S: k& f
45 30 3.5 6. 5
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) Q+ \8 K. J. {# C* g 40 25 2.5 6. 20
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/ C% [0 {4 ^' B9 j2 E2 u+ Q/ o( h* g 45 21 3. 7. 5 , I- ?3 ]5 x7 Y
45 25 3.5 6. 10
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50 25 2.5 7. 5
) F9 R4 K+ m3 D+ p, u6 J. F: ^; b) H 50 30 3. 6. 10]; t=[2.673;3.412;1.315;2.019;1.996;0.885;9.317;4.484;1.768;5.720;2.316;0.678;1.042;1.492;0.765;8.812;1.204;3.130];: W' R6 ?: y% U- n* o3 G
T=t'; P# ~& c( a* V2 ]/ U' d
P=p';7 J" K) J0 ^- _# G; a
net=newff(minmax(P),[12,1],{'tansig','purelin'},'trainlm');' M ~' j4 t7 |2 Q Q* [
%训练网络 net.trainParam.show=10;1 u+ c8 i0 X5 I7 S- Y
%net.trainParam.lr=0.05;
* H8 V* y' q6 i9 R% g%net.trainParam.lr_inc=1.05;
$ R0 C3 Y5 f! ]( o1 T, d: J" nnet.trainParam.epochs=10000;
1 e2 S" \) s- ~. }net.trainParam.goal=1e-5; ( d1 B/ K, p- d1 Z E+ y. n
% randn('seed',192736547);! d4 p. K) z1 v# D
% net=init(net); _0 c* b* \/ }$ W
[net,tr]=train(net,P,T);; C4 _" i3 y/ [9 c: H
minmax_var=minmax(P);
- w8 y" d/ D8 \) |: j+ h# {1 Fminmax_target=minmax(T);
( G' \1 N3 \1 U5 O* T9 I( W6 B( A0 nsave('result','net','minmax_var','minmax_target'); % 将网络输 转换成 [# n; x$ y0 P) m# {
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load('result');
2 m, s5 H5 h. T1 p [Pnew,endPop,bestSols,trace]=ga(minmax(P),'fitness');! Q- E) ?: U2 E
% I. u# L' f' v+ k" y+ D/ O; o
%性能跟踪2 R# o) D+ [; B! `- f! J
plot(trace(:,1),trace(:,3),'y-');6 p: _6 X: r& w& L9 ?
hold on ; Q) J) P$ l2 F- H- B
plot(trace(:,1),trace(:,2),'r-');
# {6 @/ ^+ I6 R. T2 m xlabel('Generation');
) |* w; F) ]) a ylabel('Fitness');# E) n+ a6 B5 ]) Z/ u% v
legend('change of solution','average change of population');/ e+ u. X$ ?0 J) k2 \
TRAINLM, Epoch 0/10000, MSE 12.2801/1e-005, Gradient 1739.63/1e-010
3 r8 D' {3 D8 Q3 b9 ITRAINLM, Epoch 10/10000, MSE 0.694955/1e-005, Gradient 93.6508/1e-010, k3 m0 N" `' W
TRAINLM, Epoch 20/10000, MSE 0.0242391/1e-005, Gradient 2.89095/1e-010
* z& N2 N; x* p: o2 _TRAINLM, Epoch 30/10000, MSE 0.0206875/1e-005, Gradient 4.2655/1e-010
8 X7 Q6 q! S1 c2 T- `. q/ j. U0 s$ ?TRAINLM, Epoch 40/10000, MSE 0.0185878/1e-005, Gradient 18.249/1e-010
. L9 _, C' W' f* W( KTRAINLM, Epoch 50/10000, MSE 0.00947447/1e-005, Gradient 55.0854/1e-010; x5 w! \* c8 n( T% ~
TRAINLM, Epoch 53/10000, MSE 1.24279e-006/1e-005, Gradient 0.504667/1e-010
& ], w" s! O: E) J4 p- r+ \ rTRAINLM, Performance goal met. ??? Undefined function or variable 'minmax_target'. Error in ==> D:\MATLAB6p5p1\work\fitness.m) L) y5 p$ k9 h% _4 O+ ~7 z
On line 2 ==> min_target=minmax_target(1); Error in ==> D:\MATLAB6p5p1\work\initializega.m% G9 m( v& ~0 b6 U' W# q' b$ y
On line 41 ==> eval(estr); Error in ==> D:\MATLAB6p5p1\work\ga.m
/ w' s+ u! e: NOn line 148 ==> startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2));0 K* @+ i3 I6 A7 I/ t
所使用的适应度函数是 function [sol,eval]=fitness(P,options). k( r. e' L6 R
min_target=minmax_target(1);
& y1 e( ?1 h1 D3 J) [# }, g max_target=minmax_target(2);
. _' z' d H/ G4 Y, {$ U; f2 j eval=sim(net,P)
$ N Q* q4 { g6 Y# N% ? if isformax
, V: z+ |: P2 ^8 d2 ^ eval=eval-min_target+(max_target-min_target);1 J& ~* V( k! O. ?. U5 L
else
( E' o9 v' {% X; g3 i eval=-eval+max_target+(max_target-min_target);
, f7 g$ b; n, M% \7 K4 L% G end | 
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发表于 2005-1-22 22:42
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发表于 2005-1-23 12:26
