Matlab的BP神经网络各种不同算法程序

Mhza

1:一般模式的BP:
. }$ L% ^1 a" P% iclc
P=[-1 -1 2 2 ;0 5 0 5];
T=[-1 -1 1 1];
+ k* d! ?0 S, qnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingd');
9 Q' {' E+ F& a- t% s; ^* }net.trainParam.show=50;
4 w5 R+ q6 i4 [4 B% g9 Xnet.trainParam.lr=0.05;
net.trainParam.epochs=300;
net.trainParam.goal=1e-5
' O) g% S9 T9 A( a! x[net tr]=train(net,P,T);

( ]/ D/ T2 L, C8 P2 K* d. Q$ K/ D& D2：加入动量的BP
clc
- O4 {& M4 s) V0 f/ p2 i% @P=[-1 -1 2 2 ;0 5 0 5];
+ F. I( E" n; ~8 N% I2 B1 R' K. jT=[-1 -1 1 1];
0 y( G" j7 W9 P" Anet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingdm');
net.trainParam.show=10000;
6 Z1 `4 \9 x, d& Nnet.trainParam.lr=0.05;
net.trainParam.mc=0.9;
4 J1 T0 F: s- e2 g1 F- Z  Znet.trainParam.epochs=10000;
% d* V7 \$ [1 C1 m, O2 L! knet.trainParam.goal=1e-5*100
& t& B! p& ]. Q! X[net tr]=train(net,P,T);

3：自适应LR变步长：
# u5 A7 l! n" dclc
5 [9 ]: R* F' gP=[-1 -1 2 2 ;0 5 0 5];
T=[-1 -1 1 1];
/ Y2 k) {" \/ U, a* C" n: w4 Fnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingda');
9 O8 `5 r* U; N- Bnet.trainParam.show=10000;
8 j) i$ `6 P9 enet.trainParam.lr=0.05;
1 B# E+ i% i: s1 J* r' Wnet.trainParam.lr_inc=1.05;
" M6 A: H9 M8 onet.trainParam.epochs=10000;
0 |" Y% @! {- V( W/ n: @) {net.trainParam.goal=1e-5*100
[net tr]=train(net,P,T);
* H% N; ~6 l8 ^% X6 Q, e" z+ V: P8 Y6 N
7 o3 }( ]8 }! ]; J% A1 E- ?: D
8 V0 m! N9 Z  ]1 U1 B4：弹性梯度法
- W  _: u" g* \* W0 P" mclc
& I$ T, i# U; U$ `: G+ ?( ^" c. WP=[-1 -1 2 2 ;0 5 0 5];
0 c. x# W  Q8 m6 g# M4 {T=[-1 -1 1 1];
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainrp');
2 H3 t+ k/ D8 d) ~net.trainParam.show=10000;
8 Y2 o" O, W( O' s+ d' f- Snet.trainParam.lr=0.05;
net.trainParam.lr_inc=1.05;
net.trainParam.epochs=10000;
net.trainParam.goal=1e-5*100
3 g4 O3 m% e7 E) }% X[net tr]=train(net,P,T);

4 c5 c" l" a, e1 Q" @1 s" M5：共轭梯度1
clc
P=[-1 -1 2 2 ;0 5 0 5];
" q8 b- b6 e5 y8 ]1 GT=[-1 -1 1 1];
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgf');
! E6 d+ q. P4 A& G, e8 |( Dnet.trainParam.show=10000;
% @+ o0 c) Y& v8 U9 Lnet.trainParam.lr=0.05;
9 q. c: a  W( {net.trainParam.lr_inc=1.05;
. X9 n9 ~; B! P& r+ unet.trainParam.epochs=10000;
' P) p' q, d* V9 _3 }9 F% j  ~net.trainParam.goal=1e-5*100
, A1 x+ p$ p( A  \/ U# j* S9 ?* R[net tr]=train(net,P,T);
8 ]7 |0 y" d7 N* S% ^' z1 Z
6：共轭梯度2
; P$ Q1 I* Y" R0 ?2 Z3 q4 a+ `! |clc
$ {) B/ G2 W! \& `  K  |( aP=[-1 -1 2 2 ;0 5 0 5];
$ t5 h: O  O" k' \$ j3 b8 q# HT=[-1 -1 1 1];
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgp');
: L% ^/ o0 U3 e+ vnet.trainParam.show=10000;
net.trainParam.lr=0.05;
net.trainParam.lr_inc=1.05;
6 j8 _; z  P' q* lnet.trainParam.epochs=10000;
net.trainParam.goal=1e-5*100
[net tr]=train(net,P,T);

7：共轭梯度3
8 y( A) N4 ]# ~8 g* s& T8 Nclc
' Z3 Z; H$ r4 f& HP=[-1 -1 2 2 ;0 5 0 5];
T=[-1 -1 1 1];
$ h9 r) p) V6 M: i0 F3 ]net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgb');
net.trainParam.show=10000;
# L& E' _9 A: P  {net.trainParam.lr=0.05;
* \* }5 a% x) y5 c% C7 Fnet.trainParam.lr_inc=1.05;
net.trainParam.epochs=10000;
. \2 A0 U. z: w+ q  p" v* d  jnet.trainParam.goal=1e-5*100
[net tr]=train(net,P,T);

5 W9 M5 c5 s9 ~, M( z- u8：共轭梯度4
% B! S5 ~" t4 aclc
  c+ S1 i: v: F9 O6 h$ ]" O  [3 m+ }P=[-1 -1 2 2 ;0 5 0 5];
' e2 ~3 ?1 w' ?8 U9 W( L2 mT=[-1 -1 1 1];
; P3 E5 h( i$ znet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgb');
' v6 Q1 D' y1 d/ ]  O, }net.trainParam.show=10000;
4 c, D  J( z  f/ m6 Dnet.trainParam.lr=0.05;
net.trainParam.lr_inc=1.05;
2 K. _1 C8 T: E; s5 _3 V5 Enet.trainParam.epochs=10000;
net.trainParam.goal=1e-5*100
% @- {+ [1 K# h[net tr]=train(net,P,T);
3 b4 Q7 E& \- I( f+ }: N' F) \4 @
0 z. R' L) a1 C" B9：拟牛顿法;
, d" ~: B8 B, |) p# ]clc
P=[-1 -1 2 2 ;0 5 0 5];
T=[-1 -1 1 1];
1 f6 g" S4 r5 q+ a7 N9 K$ i4 e( Dnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainbfg');
( o& [1 s" |) p& `" fnet.trainParam.show=10000;
net.trainParam.lr=0.05;
5 F* e1 C( R0 t2 h2 P# Mnet.trainParam.lr_inc=1.05;
net.trainParam.epochs=10000;
net.trainParam.goal=1e-5*100
[net tr]=train(net,P,T);

10:一步正割
clc
P=[-1 -1 2 2 ;0 5 0 5];
; @$ z* Z2 j3 i  `# NT=[-1 -1 1 1];
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainoss');
net.trainParam.show=10000;
7 D# K' T/ w9 K! |3 j4 F) @2 ?net.trainParam.lr=0.05;
' Y: ]1 z# Q: w3 B7 knet.trainParam.lr_inc=1.05;
net.trainParam.epochs=10000;
net.trainParam.goal=1e-5*100
[net tr]=train(net,P,T);
- v+ i, J- q2 i% \) V  F
" R0 i% J8 d6 o! y11：levenberg-marquarat:
/ x% s1 \4 ^$ @7 }$ ~clc
P=[-1 -1 2 2 ;0 5 0 5];
6 C* ]$ c  G4 ]0 |- a- UT=[-1 -1 1 1];
, H3 [7 I" l( m! anet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainlm');
net.trainParam.show=10000;
& i* m8 V" i6 M. e4 Z8 H" c7 ^net.trainParam.lr=0.05;
net.trainParam.lr_inc=1.05;
net.trainParam.epochs=10000;
net.trainParam.goal=1e-5*100
[net tr]=train(net,P,T);
7 b- z  ?; G$ A& x9 N
' J& b* G, C: v4 `

relchhiclty

谢谢分享