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标题: Matlab的BP神经网络各种不同算法程序 [打印本页]
作者: Mhza    时间: 2019-11-11 15:02
标题: Matlab的BP神经网络各种不同算法程序
1:一般模式的BP:
0 l* l( O! _# g4 _' Qclc# |6 Z& R+ E8 t& ^
P=[-1 -1 2 2 ;0 5 0 5];
; l# q. W+ e) LT=[-1 -1 1 1];: h1 x6 c9 `) l: v
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingd');& A$ ?- H2 o/ ~$ Q) z8 h4 \
net.trainParam.show=50;
3 q: }0 e7 |; r( E6 q6 ~net.trainParam.lr=0.05;) Y  t- n3 A7 m4 u' ~
net.trainParam.epochs=300;( C- v. S# }7 M1 C4 c4 Y
net.trainParam.goal=1e-5$ D# Z% y* k! Y3 P% F# t& n/ G" V
[net tr]=train(net,P,T);
% f0 c+ {  o" Z3 Y2 \
4 j, b* _& a6 G: o6 R; h$ U2：加入动量的BP0 m& c' \+ }$ f$ k
clc7 c3 d4 }: L! p$ O+ C2 `6 s
P=[-1 -1 2 2 ;0 5 0 5];
, {' ]4 K. b% Y1 U3 c, i* xT=[-1 -1 1 1];1 O& ]% X# K9 L* |& s7 z
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingdm');
$ x# q4 r5 K" p: c4 {) snet.trainParam.show=10000;) b/ ]) I/ Y! o/ y, }. h
net.trainParam.lr=0.05;
" F- c; W3 h% y' E0 \; bnet.trainParam.mc=0.9;
# v7 U* d# y, mnet.trainParam.epochs=10000;
( V0 R& o: l) P2 X: O: B! ~net.trainParam.goal=1e-5*100. V' w. v) z1 J8 M& W6 C" ~
[net tr]=train(net,P,T);
# n: C/ x5 ~, ?. h; Y6 W' C" T& z% L7 j! z& O% I! p
3：自适应LR变步长：1 N" s' Q) |+ [, z/ o
clc5 c! b# A) K1 t( w/ A2 |. w  R( d; f
P=[-1 -1 2 2 ;0 5 0 5];
# n( ?- A: v" B: S; i" eT=[-1 -1 1 1];
0 ]* u3 o- d8 J7 `" c3 [net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingda');
! z4 Y, b5 v! w- W# |+ Nnet.trainParam.show=10000;- M$ |% v& h6 s' @
net.trainParam.lr=0.05;
8 T$ q( B& U' z* T- R9 P/ ]3 X+ cnet.trainParam.lr_inc=1.05;8 S* A  M2 g" z
net.trainParam.epochs=10000;
$ r0 e0 R6 l% \net.trainParam.goal=1e-5*100! h' E% Z! p  L) J' B
[net tr]=train(net,P,T);
4 _3 j: K, a5 E- `# k1 c+ H. j1 I8 l$ J  l7 g! Y

; |% V6 n) j3 ]3 C6 Y4 L- I4：弹性梯度法
* n8 }0 N. I* v- Sclc% g; a& G1 O0 K0 E% y/ H
P=[-1 -1 2 2 ;0 5 0 5];- t: J+ G% ]- D% r' f
T=[-1 -1 1 1];. w# e+ k' h2 b7 ]
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainrp');
2 n( ^* ^, f1 g1 P* r3 ^9 W5 c& Fnet.trainParam.show=10000;
3 y9 j5 A& b+ C  _; b% Onet.trainParam.lr=0.05;
- O- Q4 V: O4 p* L. Y1 inet.trainParam.lr_inc=1.05;3 B) }! M7 S+ J6 T# G8 `  v
net.trainParam.epochs=10000;5 G+ S9 W4 e! Q* @! G* p/ I
net.trainParam.goal=1e-5*1005 N% w+ G0 ^+ U* S  b
[net tr]=train(net,P,T);- x8 d3 b/ U+ t1 Q0 Q7 H

9 H! H* w9 B3 Z5：共轭梯度16 y" D+ v, F1 w7 a$ p
clc3 m4 \! b* }( H! k! I
P=[-1 -1 2 2 ;0 5 0 5];
, Q+ w. q% k3 x3 b# k5 ?T=[-1 -1 1 1];& X& I: r8 W4 a" S$ X2 B6 f0 l
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgf');
4 h8 f% I) P. Mnet.trainParam.show=10000;
; ?+ V. @$ M. p8 i6 i0 g. }net.trainParam.lr=0.05;
, X1 b. w. E$ V3 pnet.trainParam.lr_inc=1.05;* R% ~$ S4 P6 J* L( B
net.trainParam.epochs=10000;
0 F* v" W; b/ d5 pnet.trainParam.goal=1e-5*100
( T2 T& R. }+ R6 D! T/ ]. G/ C[net tr]=train(net,P,T);
5 N4 {' J0 T0 g4 D7 a0 K* |! {! L
5 m8 {7 }: V4 d6：共轭梯度2
& @& j% [6 f5 F7 F9 Y3 Yclc& j/ \  S6 V5 U
P=[-1 -1 2 2 ;0 5 0 5];& y1 g9 w! `" _) F$ j
T=[-1 -1 1 1];' @. g7 X0 y% Z8 l- O7 p. U
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgp');
% l) K  b( K2 s# {8 ]net.trainParam.show=10000;& S. L3 N. ]1 R% Q4 @
net.trainParam.lr=0.05;
3 J) T( m3 _& [& M" D" {net.trainParam.lr_inc=1.05;
* g+ i9 {! Z" I& M/ Qnet.trainParam.epochs=10000;; r& w8 s3 W! v  k! @5 e( ]' Q8 z0 g
net.trainParam.goal=1e-5*100
! E2 |. r: ~( t- Z8 y* @[net tr]=train(net,P,T);/ {! @" ]/ z! A% d! e1 W! m1 \# ]. ^! ?
$ w: n# v: n- l6 W# V
7：共轭梯度3
) g/ e2 W1 @6 {8 @8 k/ }9 A, m) Fclc# |3 T% m' w, K+ ]  [# d
P=[-1 -1 2 2 ;0 5 0 5];
+ |  [$ ^) F& G" o' f# i6 z/ ET=[-1 -1 1 1];3 M5 l; }( v; t! X' V* x
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgb');9 F0 N4 U2 i( J! q5 Y
net.trainParam.show=10000;  ~' p! s: g' O+ F5 ~$ w
net.trainParam.lr=0.05;: ]; t+ Z- q" _, a  u2 f/ u
net.trainParam.lr_inc=1.05;
" b- E  n9 [# N9 p8 H1 o) D& |9 Jnet.trainParam.epochs=10000;  k+ C6 {% [$ `. W. q- D" j6 R
net.trainParam.goal=1e-5*100
7 I, c( W6 t9 Y' J- g* p  X[net tr]=train(net,P,T);
# D: @+ H! ^' p+ z' c( E. k% ~5 ?* n* |
8：共轭梯度4
( P0 Z/ B6 t9 p( y7 B+ zclc
+ e+ K7 s6 E9 P5 L9 SP=[-1 -1 2 2 ;0 5 0 5];' r5 B! W" b# ]" j% q
T=[-1 -1 1 1];- W. Q4 f, _* _; y% H- @6 J% [
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgb');  A( ~6 X4 K/ r
net.trainParam.show=10000;2 T, A6 D6 @+ _! o) K8 w
net.trainParam.lr=0.05;8 \. {. y4 ~$ C; {- U
net.trainParam.lr_inc=1.05;
/ D8 q; k$ }" Anet.trainParam.epochs=10000;8 Y( I2 l' O: m9 @4 U  C
net.trainParam.goal=1e-5*100. h  x& V9 o% P7 v- P
[net tr]=train(net,P,T);3 J( Z0 ]8 z9 B, V; |: I& _
: C( P$ f% K# ^; k
9：拟牛顿法;
' M3 u" [) q; v1 Iclc
7 [( |+ W$ w3 TP=[-1 -1 2 2 ;0 5 0 5];" A* N+ A; f4 y+ H* ^. x: M! v; y+ ^
T=[-1 -1 1 1];
; }4 k- r6 Z' k0 y  Tnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainbfg');8 S7 r' z0 C; S4 K+ r
net.trainParam.show=10000;' m; J( V* A+ @& n# c& p' f& c
net.trainParam.lr=0.05;
# n8 V$ a9 |; x/ |# snet.trainParam.lr_inc=1.05;! B" D2 V7 `! n! D
net.trainParam.epochs=10000;
4 f  W' D6 D) L0 _- gnet.trainParam.goal=1e-5*100; U/ \4 y; t9 @; U, Z  E
[net tr]=train(net,P,T);/ S1 W# A4 G! e' E& {1 ^) A6 ?& a' P

" \1 L" F. a# s) W8 _" e" h, E10:一步正割
' n) t, v! L+ g4 Gclc9 K* p/ A% g8 C3 X) |2 L: v
P=[-1 -1 2 2 ;0 5 0 5];
* f5 @0 a1 X. w$ [5 qT=[-1 -1 1 1];
2 ~3 e1 }8 D( _$ q9 B' f) Fnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainoss');
, k  _& k7 }) S9 h6 D3 q) g0 U2 Hnet.trainParam.show=10000;
% x& q2 }7 x2 o& ^1 W) dnet.trainParam.lr=0.05;
  M; W4 L1 b! r# _+ d: Cnet.trainParam.lr_inc=1.05;
$ \3 O7 A6 m) [* Pnet.trainParam.epochs=10000;, q% _) x+ h$ ?2 D/ V1 ~  c) A
net.trainParam.goal=1e-5*100% L3 [& Q- r3 L8 _% C
[net tr]=train(net,P,T);4 m5 }$ \2 L5 L; W# q+ I
  S) I" N! H1 x, {
11：levenberg-marquarat:
6 P+ h2 W2 L! y$ t, T5 g7 gclc, r0 |" o. {1 {  ?9 S, u
P=[-1 -1 2 2 ;0 5 0 5];. _8 ]& d2 X, Y9 _2 |+ v6 e( }
T=[-1 -1 1 1];
  S. J7 J# R9 inet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainlm');
3 l" u1 W7 r+ n' i+ {& p4 }net.trainParam.show=10000;" N0 L- X; z6 ]# k1 L7 s; Q( Q2 \
net.trainParam.lr=0.05;
' g+ `  N7 J0 I9 b/ Gnet.trainParam.lr_inc=1.05;
3 x5 M1 o* \3 f6 y5 anet.trainParam.epochs=10000;) }% r; y& i2 D9 g
net.trainParam.goal=1e-5*100
- l  r& a; E& C$ ?9 E! ]$ o[net tr]=train(net,P,T);
0 w9 x' }* ^8 q# O, Q: m; W( I2 v) B$ E3 i; b/ n2 J, I
作者: relchhiclty    时间: 2019-11-11 19:16
谢谢分享



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