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1:一般模式的BP:$ t/ k F# y% Z
clc9 x7 l+ n* P- w; x7 V! N
P=[-1 -1 2 2 ;0 5 0 5];
y$ ^' G! s& rT=[-1 -1 1 1];
) g- @4 X2 A, e& ?net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingd');, a7 w! l; \+ [" i; o7 W9 I! |
net.trainParam.show=50; T4 q0 b F, r( a2 l( H
net.trainParam.lr=0.05;
3 v& m! k% @% J! A B unet.trainParam.epochs=300;
2 ?" X2 {1 h7 Enet.trainParam.goal=1e-5
* W: }4 y* }: L" Z! l[net tr]=train(net,P,T);/ }$ P( q6 C* ~) `/ m( _& F [
. v9 o; s2 t! I* q
2：加入动量的BP9 O1 }+ x8 ?. ~ ?- N- ^
clc
3 F) {/ O3 P$ n2 V/ y3 B5 NP=[-1 -1 2 2 ;0 5 0 5];
) Q1 L7 j& H, A0 i1 O+ @4 WT=[-1 -1 1 1];2 i& ?1 n' J) s* j, K- c4 p; M4 G9 {
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingdm');
8 Q+ q2 E( I7 inet.trainParam.show=10000;
k/ U: D$ i6 y& F4 K9 |9 v; Xnet.trainParam.lr=0.05;/ G! v1 n' {5 W3 E4 M* v3 N7 q* }8 `
net.trainParam.mc=0.9;
$ I3 G" ]$ c: B; C+ Xnet.trainParam.epochs=10000;5 a$ Q! c' S A1 v* {
net.trainParam.goal=1e-5*100
- s: P/ T/ ~/ \( [9 Q, v( u[net tr]=train(net,P,T);( X, C0 k' D( R0 P7 l9 `
, }3 r/ @0 A3 L- H2 l- [/ T" P" Y
3：自适应LR变步长：9 G- P. V' w9 f* R* l4 c
clc
+ C6 D3 \, o3 X HP=[-1 -1 2 2 ;0 5 0 5];
2 l$ n. I9 Q6 a4 f; G3 v- w% q4 O9 iT=[-1 -1 1 1];
4 R5 p5 _7 `. R+ Onet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traingda');' Q z( M: p" }0 n# U9 h
net.trainParam.show=10000;+ t7 K- h3 }- z/ E: y
net.trainParam.lr=0.05;
9 _; I; o( ]1 K v9 {8 F: J; P: vnet.trainParam.lr_inc=1.05;
; U9 P5 m- [) T) i+ Jnet.trainParam.epochs=10000;) Y) B9 Q1 x4 \1 N5 f
net.trainParam.goal=1e-5*100' z3 Q# ]3 Q! Q" o/ p
[net tr]=train(net,P,T);
5 w2 V( @5 b* \
+ n# }7 a5 Y/ i/ V: e" x% U7 @9 U5 g: F3 `/ `! P
4：弹性梯度法" Y. m r' R/ t G9 h2 @; X& l
clc
: Y. U! o. a5 H; d2 W/ hP=[-1 -1 2 2 ;0 5 0 5];
7 O- ^! ]) l: ]8 W3 e) a3 v5 \T=[-1 -1 1 1];; Q- F: C6 w; L6 N( d
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainrp');9 Q) p5 [% ^, R4 ?
net.trainParam.show=10000;
9 p$ v% x$ W. n9 L! c' e8 cnet.trainParam.lr=0.05;+ U/ R3 k w l( A* B! f" y
net.trainParam.lr_inc=1.05;
! I( j/ {1 v2 D$ ^: Xnet.trainParam.epochs=10000;# l1 o7 a3 m! ~+ e! Q/ \
net.trainParam.goal=1e-5*100/ Z7 K4 E7 c3 f# s
[net tr]=train(net,P,T);6 O3 K2 {8 N2 S& K+ D4 U
& d1 I& g! s4 F" S6 F) ?; F1 v b5：共轭梯度1
' Z7 r% }' `8 k; V3 O) Cclc
, P3 ^- k/ K N1 S. z, e- `( |P=[-1 -1 2 2 ;0 5 0 5];
0 r0 }* Q! u: ~0 X) `T=[-1 -1 1 1];
/ h7 F ?) S/ O; k5 s" y$ Rnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgf');" h$ |" x3 o* N+ }) R
net.trainParam.show=10000;9 ?( V6 H# r a0 M$ d
net.trainParam.lr=0.05;
9 }) P) Z- R* R$ Dnet.trainParam.lr_inc=1.05;4 l6 n! Y$ Q* i+ j
net.trainParam.epochs=10000;
+ R( ^ Z: f8 r: rnet.trainParam.goal=1e-5*100
. l/ k0 _2 F3 ^# \ E% v& F& e[net tr]=train(net,P,T);
5 q$ F+ }7 c3 T& b- z$ x% c" E: c
- m9 O* x6 G. m& R6：共轭梯度2( S9 \# ?& @2 J% \7 \+ E) Y
clc
+ _0 Z$ `7 x( H3 g: b- IP=[-1 -1 2 2 ;0 5 0 5];1 O; ]( E+ Z% |/ ?& i
T=[-1 -1 1 1];
$ N4 e X4 w9 s6 xnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgp');7 l! R/ B& g% m+ {: t/ W9 }1 T
net.trainParam.show=10000;
) ~2 x7 J2 E: a% c% j+ gnet.trainParam.lr=0.05;+ y* U2 W$ g/ A# n
net.trainParam.lr_inc=1.05;$ h. e5 k* P+ L. H; M
net.trainParam.epochs=10000;
/ C' \9 e9 W4 F/ snet.trainParam.goal=1e-5*100
8 D2 r& @% d) [" ^! g& f: @ P[net tr]=train(net,P,T);
1 X( |7 M( o. P4 n! K4 F
) `4 C8 S, l7 J; A7：共轭梯度3 [) m6 w. T* k) Q& \
clc. _ F6 h4 ^' o; v3 ~
P=[-1 -1 2 2 ;0 5 0 5];- p$ }) V: D; V
T=[-1 -1 1 1];
2 O x9 X/ Y2 L1 k) ^net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgb');8 C3 S" j) K- Y. N" D8 U! |
net.trainParam.show=10000;3 G+ }/ L8 V0 C6 F) y9 x
net.trainParam.lr=0.05;" Y5 {2 U' Y; i5 ?
net.trainParam.lr_inc=1.05;
O) ^/ \, {: P. ?' D$ |# Q; knet.trainParam.epochs=10000;* c5 v( p7 }8 n# [
net.trainParam.goal=1e-5*100
4 H% |( L; E4 c) ~# I; D! N[net tr]=train(net,P,T);
, [7 K2 S4 y; M" v1 m' {' s4 t" f/ {9 s; [) {
8：共轭梯度4
6 C! F7 A; Z% }1 Wclc- z' M, D2 @& z/ g0 C
P=[-1 -1 2 2 ;0 5 0 5];
. {6 o* [# l$ r* J) @8 R# UT=[-1 -1 1 1];+ \% d% L( B( b# K5 g3 ^1 j6 G
net=newff(minmax(P),[3 ,1],{'tansig','purelin'},'traincgb');
4 q+ y0 E+ d9 R, W# wnet.trainParam.show=10000;
4 R ^1 m; F4 {5 i1 N( ^" i& Cnet.trainParam.lr=0.05;) u1 u; n4 |$ Q: ?1 P) V1 r9 @
net.trainParam.lr_inc=1.05;6 ~6 W/ Y# l2 e) d- U
net.trainParam.epochs=10000;
1 y6 F1 K" x+ k' _- hnet.trainParam.goal=1e-5*100
/ C( T0 k9 K ^- ?5 p3 u2 T[net tr]=train(net,P,T);6 o/ a# Y5 {; ?# ]
9 J G W2 a) S
9：拟牛顿法;" W1 ~' n x9 ]- Y: m
clc
9 o( ]" T8 q# O# x5 mP=[-1 -1 2 2 ;0 5 0 5];
( O+ N, S$ n1 a" sT=[-1 -1 1 1];
$ d, o3 u$ ~6 Xnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainbfg');
) u$ l6 L# N8 c S% L& Wnet.trainParam.show=10000;5 c, E. \1 q" q( y# Z) N v
net.trainParam.lr=0.05;; K2 U, N Y1 R! [
net.trainParam.lr_inc=1.05;- q3 |; g/ F) s4 I
net.trainParam.epochs=10000;1 R9 u* O: W* c/ R+ |( ~
net.trainParam.goal=1e-5*100
2 `7 g! S! P# R# |& e0 V# L[net tr]=train(net,P,T);
9 h) ~1 ^9 n3 ^. C) {, V, L% g1 L8 _, R s% z8 W
10:一步正割
3 F9 x9 e* D- Q E# B/ n: \/ p. Iclc+ V$ e; x& w2 U! P$ [9 |5 H
P=[-1 -1 2 2 ;0 5 0 5];( Y# M6 a" q* n: b1 i
T=[-1 -1 1 1];
$ s/ o* b2 h( K9 \. Vnet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainoss');
: n: S6 d4 a, W2 Z* ^8 K& _* qnet.trainParam.show=10000;
+ I5 K8 D. V; U( n, g7 Nnet.trainParam.lr=0.05;
" e# n: d% w, b- \* p g# Rnet.trainParam.lr_inc=1.05;5 t6 y# M9 j. v) B- v
net.trainParam.epochs=10000;5 f9 e; _7 G% e" q5 ^* Q- y+ g# c
net.trainParam.goal=1e-5*100" h7 F7 @ V: @8 Q6 o( `
[net tr]=train(net,P,T);
8 J, t4 l2 l$ q3 k# o- g2 h- M+ }- u! n- r/ y* _, ~# z! v
11：levenberg-marquarat: R3 m& v# l1 W/ n+ o7 i; j4 B
clc j1 K/ X" U. Q) ^( W# _4 y' s
P=[-1 -1 2 2 ;0 5 0 5];0 G' C; o! Y2 f( r
T=[-1 -1 1 1];
% r* H7 m6 q- E" c- S7 E3 z+ c; knet=newff(minmax(P),[3 ,1],{'tansig','purelin'},'trainlm');
) u7 u. K& Y) |+ |5 nnet.trainParam.show=10000;/ c( x. m8 Y7 G6 @
net.trainParam.lr=0.05;. b- H+ S- I! m$ N- M3 o
net.trainParam.lr_inc=1.05;
% C# ?/ b; B1 \# ]7 Wnet.trainParam.epochs=10000;" G* J% ~$ M& M' v) l! w; w
net.trainParam.goal=1e-5*100& M" [5 n. Y% {% w E& e
[net tr]=train(net,P,T);
* V% I8 i X$ B! U+ _5 B3 V, E w0 r6 @
% ~: D6 K' G/ {9 s6 H. I4 H |
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发表于 2019-11-11 15:02
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