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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 - u1 N6 R; |7 z. L
40 25 3. 6.5 10
! `* k/ y, W9 C; m 40 30 3.5 7. 20
$ D1 f+ ~" s" l4 N/ }" v: C0 H 45 21 2.5 6.5 10
* ?0 _) A* S5 ?6 D 45 25 3. 7. 20
8 ]8 ], m. z% t- a7 X) ? 45 30 3.5 6. 5
$ p" `+ \1 Q& K: Z 50 21 3. 6. 20
5 T0 r1 ]+ \# v+ S1 B5 O 50 25 3.5 6.5 5
& @, o- i+ ^ ~4 D) V& d 50 30 2.5 7. 10
0 o, \2 w8 g7 g2 \- { z$ a# e4 n7 | 40 21 3.5 7. 10 $ J$ j# o! D8 o; @: V3 | q
40 25 2.5 6. 20
" Z2 w3 u d& ]8 j* t6 [+ a 40 30 3. 6.5 5
# h F( O* b1 M- k4 a 45 21 3. 7. 5 $ o: a! I$ N6 G4 l, r! r* I3 i
45 25 3.5 6. 10 8 p$ |3 O+ F! g
45 30 2.5 6.5 20
' E' d6 e1 p4 V0 e0 i( h- h 50 21 3.5 6.5 20
; I; {# n1 H3 M- C* v9 `. ]7 D 50 25 2.5 7. 5
- ?9 b/ F2 Z% |1 e+ L0 U& [ 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];; N1 x- `! x, L" _2 `9 r
T=t';) j: s& z7 W; o# A
P=p';
& L! \1 U: T" |3 a) ]3 Tnet=newff(minmax(P),[12,1],{'tansig','purelin'},'trainlm');
7 _9 T/ g0 L& ~! e0 z r D%训练网络 net.trainParam.show=10;
! Y! _- x/ ?2 {%net.trainParam.lr=0.05;
/ o% ?! ~; k* ~5 W# f$ J%net.trainParam.lr_inc=1.05;
2 H0 b3 D$ W3 ?net.trainParam.epochs=10000;
9 ~& m4 s6 e2 m7 Z m4 C% ^; dnet.trainParam.goal=1e-5;
* q5 j5 l7 x0 M4 f0 |2 p+ I- a& x% randn('seed',192736547);& z/ j# [; R D. `1 g+ l! {6 \( T Q( Y
% net=init(net);6 T6 _# n) h# t! b r- v
[net,tr]=train(net,P,T);1 u" K; ^0 @) k% l
minmax_var=minmax(P);" B) i6 v0 A; _& ~6 g4 |% T! z
minmax_target=minmax(T);- {2 R" H6 e* p6 `1 K
save('result','net','minmax_var','minmax_target'); % 将网络输 转换成
0 ~% w/ q8 i0 @) `% k* m3 w5 {; C / I5 B! a' N2 f2 X" c, p
load('result');8 L1 a- ?& R7 A M
[Pnew,endPop,bestSols,trace]=ga(minmax(P),'fitness');
) P5 j" P/ \' F0 o
2 z; [$ V6 k V1 r' G' W7 j7 Y %性能跟踪5 E8 o; Q- m9 x4 t9 S# r
plot(trace(:,1),trace(:,3),'y-');
! H% h. n* s+ }+ I) D* G& t0 [ hold on - a3 B3 e9 j: s: h) L
plot(trace(:,1),trace(:,2),'r-');
8 `" U. k1 D2 u$ c; k xlabel('Generation');
0 m6 S" e8 Z. B ylabel('Fitness');
: \# D: j. w, u( B. h2 G legend('change of solution','average change of population');
4 q6 U1 k8 |5 _$ A& aTRAINLM, Epoch 0/10000, MSE 12.2801/1e-005, Gradient 1739.63/1e-0108 b& d6 ]. M' u! S' k
TRAINLM, Epoch 10/10000, MSE 0.694955/1e-005, Gradient 93.6508/1e-010
; J% I+ [! f; w( c& qTRAINLM, Epoch 20/10000, MSE 0.0242391/1e-005, Gradient 2.89095/1e-010- |7 a( S8 H$ V& B
TRAINLM, Epoch 30/10000, MSE 0.0206875/1e-005, Gradient 4.2655/1e-0102 t% w( |9 |8 {0 m& ?8 e
TRAINLM, Epoch 40/10000, MSE 0.0185878/1e-005, Gradient 18.249/1e-0105 U2 O: D( k4 _6 i `
TRAINLM, Epoch 50/10000, MSE 0.00947447/1e-005, Gradient 55.0854/1e-010
( D* N, N% b2 _8 x6 UTRAINLM, Epoch 53/10000, MSE 1.24279e-006/1e-005, Gradient 0.504667/1e-010
- C/ ~4 w7 z+ STRAINLM, Performance goal met. ??? Undefined function or variable 'minmax_target'. Error in ==> D:\MATLAB6p5p1\work\fitness.m7 E0 }$ ]: G+ v" b5 ~
On line 2 ==> min_target=minmax_target(1); Error in ==> D:\MATLAB6p5p1\work\initializega.m9 E, m, F3 {! w0 e1 D
On line 41 ==> eval(estr); Error in ==> D:\MATLAB6p5p1\work\ga.m$ O, ]! I5 a# ^
On line 148 ==> startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2));
: U9 c. b7 o& Z6 E4 L" G所使用的适应度函数是 function [sol,eval]=fitness(P,options)
8 t1 f+ N6 W, N6 p min_target=minmax_target(1);* H( b, J1 C- p) B% [5 x5 u. E
max_target=minmax_target(2);
6 \9 O. z" d+ t# f- Q0 P2 ` eval=sim(net,P)7 r) n$ V Z0 x: Z" ~* [2 z
if isformax
: A* m6 J4 B- o; R( P eval=eval-min_target+(max_target-min_target);
) g+ Q+ }& U- ~, | else
3 h) r! ?, W8 {: _/ a9 F5 o) P M eval=-eval+max_target+(max_target-min_target);
4 \0 L, N$ `! l5 B 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
