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升级   48.42% TA的每日心情 | 开心 2016-11-7 00:15 |
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签到天数: 7 天 [LV.3]偶尔看看II
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一 基于均值生成函数时间序列预测算法程序6 y% E' ~: q# h; V
1. predict_fun.m为主程序;
7 m; L9 j6 A+ }. ^; y/ N2. timeseries.m和 serie**pan.m为调用的子程序8 g7 A" Y7 o. j' }5 i2 ~/ x8 D& y
5 ]! i t$ H( O% pfunction ima_pre=predict_fun(b,step)
3 j* J( \& \ W9 y; H4 m e% main program invokes timeseries.m and serie**pan.m1 O% A1 {4 q9 ]; f
% input parameters:
- {, Y( g) P. P$ l: G2 B- N8 `6 e. g" ]% b-------the training data (vector);
! U% a& C0 Q4 i) G9 q% step----number of prediction data;
! i* u8 l4 u5 P3 q9 A% output parameters:
3 Q) M# a! D2 l( p* l% ima_pre---the prediction data(vector);( I' T1 e) o Z) \5 l) C/ u
old_b=b;
9 V4 a9 w- d2 w5 m# ]* t Y* v$ Emean_b=sum(old_b)/length(old_b);
& z9 u B, t4 W! I/ G' Istd_b=std(old_b);
, a% u) D. j, @* zold_b=(old_b-mean_b)/std_b; T( }; r, z6 \
[f,x]=timeseries(old_b);
& |. a" f" G, u- U0 d9 o: Nold_f2=serie**pan(old_b,step);
5 F, z- M6 A# P+ \; |% f(f<0.0001&f>-0.0001)=f(f<0.0001&f>-0.0001)+eps;" m9 j K2 q- L4 d9 v) h1 M
R=corrcoef(f);4 O9 @- @" m1 Y% [/ C; z2 L1 o; Q: C+ _) \
[eigvector eigroot]=eig(R);4 J) X8 S* O: `6 a0 F" R
eigroot=diag(eigroot);- O6 _& F/ I7 M4 V+ [ L% m1 R
a=eigroot(end:-1:1);& \1 j1 ?! N% _
vector=eigvector(:,end:-1:1);7 a% Z3 Q' P/ q r3 T: ~' |
Devote=a./sum(a);# n5 v( y& z& Z+ A7 `9 L
Devotem=cumsum(Devote);- n1 x( G( x E% a8 \* U, Q9 J
m=find(Devotem>=0.995);
9 g6 _& _5 F6 o8 n/ V+ Mm=m(1);$ a: p5 _7 N$ k. O! H6 |5 z7 H
V1=f*eigvector';, b+ O/ f# l& G0 N( c j, E! s
V=V1(:,1:m);
$ l0 j3 D; }0 g0 k9 ^% old_b=old_b;
9 F& B& \5 P& F; Dold_fai=inv(V'*V)*V'*old_b;; O3 ^) _/ N4 k" S+ u
eigvector=eigvector(1:m,1:m);9 O: f' O. y3 [
fai=eigvector*old_fai;
2 T* M, b, g7 L4 {f2=old_f2(:,1:m);
/ Z8 A* s( ~; y- \ Y2 E4 [predictvalue=f2*fai;- F6 V/ P) v7 ?
ima_pre=std_b*predictvalue+mean_b;
) q- ^$ V* f) ^( p& J/ r
% J/ E5 X* H) D# |1.子函数: timeseries.m - C' x# a/ |, B; O9 p
% timeseries program% V9 y. t$ s+ s, \* J
% this program is used to generate mean value matrix f;
, Q# b' I! k* _- gfunction [f,x]=timeseries(data) 2 C8 c% Y% S: B; g# o
% data--------the input sequence (vector);
! u, q1 M; R$ B% f------mean value matrix f;
: f) k) D& F9 a5 o- En=length(data);
! f* }8 g6 W2 |8 \8 K4 r0 gfor L=1:n/2
* P+ {/ P% x# q; B2 ^ nL=floor(n/L);
4 T& \( _) P" S6 f% O7 e for i=1:L
# B4 S) ^/ c: T5 Q- e; Z( H- m _ sum=0;
2 {2 B" E3 A5 M* a for j=1:nL
+ h: B% o9 Y, m. w sum=sum+data(i+(j-1)*L);
) ]' M$ \% g8 I1 S4 h end8 ] n' D: o5 m8 J+ C3 y8 v( M
x{L,i}=sum/nL;
6 h$ l# Q0 K$ C: d end$ @; E& p6 h9 W- m6 Q% \4 [/ V- u
end" G [3 _: X! ?1 [
L=n/2;6 V: u3 D* L4 T! P$ r: j
f=zeros(n,L);
- m# U. a; P2 t4 {for i=1:L9 K5 F; i) Q/ h1 _& `8 f9 _+ D, _
rep=floor(n/i);
) v& y! |3 n, r5 `4 }5 G9 o' F; x res=mod(n,i);
- w: S& v; ?$ g$ M/ v/ Z6 \* l b=[x{i,1:i}];b=b';
% C, F; E; `% Z7 V$ e% q5 g f(1:rep*i,i)=repmat(b,rep,1);& r* T$ l* B0 h5 d
if res~=0
0 g) s, W" {, t0 m7 |8 @" [ c=rep*i+1:n;
" u: F/ m% H& b5 j8 i1 e! u- m! ] f(rep*i+1:end,i)=b(1:length(c));) P: t6 r) F6 l: Z/ ]; I
end
( _! i4 g9 Z: |" eend2 A2 {6 |, {0 z5 w+ a+ }
7 |% Y5 c7 p* N `' D; U" M
% serie**pan.m
9 w: ~+ {; n: ]4 {% the program is used to generate the prediction matrix f;
F2 n) M" z6 V! a2 i2 x7 q% l" r: F2 Ufunction f=serie**pan(data,step);
( k! h, T2 I5 y2 S$ @. |%data---- the input sequence (vector)1 s; r" _2 ~+ `3 {
% setp---- the prediction number;
! X/ l4 z1 i1 P& En=length(data);
: F/ q7 j1 Q/ g3 H3 r4 `, `* @, x+ Yfor L=1:n/2
1 _: x8 @2 A; ]* A2 S) V5 B9 x8 J nL=floor(n/L);
9 {- u* y; I ~# h' ]( l1 G. w for i=1:L7 E6 O; F* y l5 f0 {
sum=0;: ?+ s- p, y. P. M
for j=1:nL7 B7 G7 p- n& x
sum=sum+data(i+(j-1)*L);% ]4 N4 S- @" \! ^0 _3 x5 b
end
% I2 x8 r3 J2 L x{L,i}=sum/nL;
% z& j# ~% @; P end1 a* ]! e4 x) p9 Q7 X& J
end
% s6 Y5 o; k/ b/ }3 J! PL=n/2;: v+ ]8 P! k+ o6 w% n, Y& _9 ~2 m* m
f=zeros(n+step,L);& N/ i) ?7 ?8 l3 g2 m( g! z* Y
for i=1:L
- e: r) h8 i8 P% _. d3 I rep=floor((n+step)/i);( k& z6 N' @& U/ W! b8 G3 c: s, K
res=mod(n+step,i);
4 q1 h' u, o2 x: U b=[x{i,1:i}];b=b';, }$ Y( a( T! ^ Z$ Q
f(1:rep*i,i)=repmat(b,rep,1);
" F# ^8 D0 b# u' C5 b if res~=00 C/ b* ~4 Q5 @# p7 K4 Z
c=rep*i+1:n+step;5 W0 E) J1 G: w. [, X& l) A
f(rep*i+1:end,i)=b(1:length(c));
' H3 X' D! X7 V% Y end3 \8 k) n& a4 Z" m( Z6 `
end
, g- ^! h+ A6 ] k: k
2 J& i9 R9 t% _二 最短路Dijkstra算法9 h1 {* h a* |7 z& U
% dijkstra algorithm code program%
5 x4 n, d8 N: o% the shortest path length algorithm& }" J5 x8 K" x% f' b# v) h
function [path,short_distance]=ShortPath_Dijkstra(Input_weight,start,endpoint)0 E! J( Y) c: ^
% Input parameters:; a% q- t0 Z( y* T! |7 a9 W
% Input_weight-------the input node weight!
W; I( K9 g, u' _, o% start--------the start node number;7 `0 o- H7 N6 Q7 B5 p% y" }
% endpoint------the end node number;
$ ?2 l' V! o ^: O- s2 l/ r% Output parameters:$ W5 F9 w, T0 Y$ w( _1 w$ E" z ~2 {0 _
% path-----the shortest lenght path from the start node to end node;7 D+ w, `7 E; X, Z6 N
% short_distance------the distance of the shortest lenght path from the
4 c! `; m9 v8 _) Y& r% start node to end node.7 i4 z& {3 a, C9 ]
[row,col]=size(Input_weight);
8 k9 S/ O$ a/ F% A; a
8 q( ^( r8 d( [/ @$ j8 p. s& B, }%input detection; X T( ~" o) d+ i B) n
if row~=col" A/ i8 P1 q2 L
error('input matrix is not a square matrix,input error ' );' \7 S4 d, \* _& l
end
$ j7 v. ~7 f; A6 eif endpoint>row
* g" Y) N8 e t error('input parameter endpoint exceed the maximal point number');( l' l+ p6 Z* q2 {; R& X
end* m7 M! A/ ^) v2 O% u6 s Q# j" ^
8 G4 t- @# o+ i+ ~' B+ @( `1 m) W%initialization
; m$ [0 z! j9 u/ r. A) Rs_path=[start];
1 z3 N. r) f9 s. Y8 a2 S. H2 G, fdistance=inf*ones(1,row);distance(start)=0;
+ V/ P( W5 B( r D% S- k* Yflag(start)=start;temp=start;% E% Y! r) s4 q* A
* }/ A$ z; E# J9 }4 x9 r
while length(s_path)<row7 e7 j; N8 Q8 ~3 P- {
pos=find(Input_weight(temp, : )~=inf);
( m' b: x5 p3 }/ \& F. K for i=1:length(pos)
9 [- @1 N/ d, v/ ]4 X- v if (length(find(s_path==pos(i)))==0)&
3 ^ j6 k1 t( E1 l: m- p(distance(pos(i))>(distance(temp)+Input_weight(temp,pos(i)))), R, P; L% {( q0 i
distance(pos(i))=distance(temp)+Input_weight(temp,pos(i));1 H* E% o# \2 x# |& G, R+ K' U( d
flag(pos(i))=temp;
- F0 o O" _ t+ ?8 |, p% B end4 T$ A! j: `5 Y/ n$ H$ A! y
end4 \1 g' i' L" ~/ q* F
k=inf;
, H$ w6 m- p4 h" j9 K1 ~; V for i=1:row
' M5 F+ }, F0 i if (length(find(s_path==i))==0)&(k>distance(i))! t8 o9 R L0 K7 a B
k=distance(i);
% J2 g4 ~* o4 y7 e7 k, t/ R temp_2=i;; o( I, [- x. \3 q& n# I1 }. P8 `
end
& ^: D) H, E, _ end
5 b- ]' n; P1 R& b6 h( b s_path=[s_path,temp_2];
- C7 n+ F w5 [9 ~1 t9 k* R- Z temp=temp_2;4 u* L7 R O6 }3 V5 U6 m! x* Z
end* u: b4 z6 t* `: }( J& `/ S0 e# o
. _1 d, h. X: Q: W: x7 G. m7 b+ g
%output the result
- ?. B. a3 d) z2 Rpath(1)=endpoint;
) `0 ]$ v4 C: o! v- H' Ui=1;: }1 P4 v' U( B* _# K* ?2 R
while path(i)~=start
" S: K) U4 T; v) z+ @) a path(i+1)=flag(path(i));6 m7 }! V* B2 h0 L# q. b
i=i+1;* j* r H9 e4 C' w5 P6 ^# }
end
1 K" R2 |% S! l m3 ?$ Vpath(i)=start;. E8 c/ S* P, Z" f9 X+ D8 h
path=path(end:-1:1);
/ H' p+ l; N9 w8 N0 Ashort_distance=distance(endpoint);
3 Y; d% R) z6 h4 u( N& g. l" p$ M三 绘制差分方程的映射分叉图
& n8 ?. m/ k/ {$ _+ h2 c$ H7 A
. p4 W6 B9 H. f# }6 q# Ifunction fork1(a); ( c5 _6 g1 O. F2 ]
b! M6 F: ~0 G8 ?
% 绘制x_(n+1)=1-a*x^2_n映射的分叉图0 P( t& _" o0 G9 r9 O/ t! m
% Example: $ O) c+ h S$ S
% fork1([0,2]); . J0 {8 ?8 ?7 c; [4 F+ P1 h+ W
N=300; % 取样点数 ) Z0 k# E1 U' P$ Q
A=linspace(a(1),a(2),N);
) r) B, D! s" f dstarx=0.9;
( Q: |5 g5 j8 t0 b" WZ=[];
$ u6 ^; @/ a3 U( H9 J5 qh=waitbar(0,'please wait');m=1;4 c% ~5 d4 t0 |8 _3 ]4 Z, N
for ap=A;
2 h2 n( ^& F! g x=starx; : v9 n7 P0 i- \+ q1 |: k
for k=1:50; % V2 z5 `1 a: Q& N: x( \
x=1-ap*x^2; - H8 f! ~4 E# z# {) O0 Y5 ~
end
2 o; ], Z& U- o for k=1:201; 5 n/ O4 Q s( _
x=1-ap*x^2; ( d4 C) M) M# y) ]/ Q+ \
Z=[Z,ap-x*i];
& I4 X* m( m2 ` end : L8 u; x {) j
waitbar(m/N,h,['completed ',num2str(round(100*m/N)),'%'],h); z! z$ w, ^2 N1 P
m=m+1;+ j& r6 R; K. s' K" F" G: F
end 9 U+ i/ q0 J8 B+ ?4 }. W. j; V, q
delete(h);0 R. j5 y$ `0 ]5 j! F
plot(Z,'.','markersize',2)
( @% c1 A0 x) `* Y) B: E1 Ixlim(a);; y# a* Z9 j$ ]5 e8 D5 a
, I' e4 K: E0 a0 o) T' n四 最短路算法------floyd算法) Z1 [$ V2 v) @4 j" m
function ShortPath_floyd(w,start,terminal)
3 r- s) Q. d- P& w, I+ {* i6 M%w----adjoin matrix, w=[0 50 inf inf inf;inf 0 inf inf 80;
! K4 b5 r6 w/ E, ?$ H%inf 30 0 20 inf;inf inf inf 0 70;65 inf 100 inf 0];
% U6 k, G3 d! W+ v! G4 B%start-----the start node;
* L+ v% D2 C% n C9 o% O. _%terminal--------the end node;
( T b' \; ^& `' C4 j; rn=size(w,1);
6 z, E5 i% ]. }/ R0 x7 }' M[D,path]=floyd1(w);%调用floyd算法程序
* e. Q2 c7 w" v: p6 ^. F0 W4 Q' h; n) \- U% U! d
%找出任意两点之间的最短路径,并输出& @! F. X: m( p c) m
for i=1:n
" t0 S$ m n9 m$ Q$ A for j=1:n7 v& J9 R; r- W. ?0 ]! f
Min_path(i,j).distance=D(i,j);) K3 E6 K& k) o3 q0 `
%将i到j的最短路程赋值 Min_path(i,j).distance# E% `# \ P2 P' e9 Q5 P9 L
%将i到j所经路径赋给Min_path(i,j).path# C6 |* a C& X! `& r3 L; e
Min_path(i,j).path(1)=i;0 s- i, b5 @- ]4 \
k=1;
. ]" j* Z) _7 d7 {9 R& j while Min_path(i,j).path(k)~=j. w! z+ d8 e* z$ \4 r- X
k=k+1;
" K( L" a" V* L7 c4 Z! D Min_path(i,j).path(k)=path(Min_path(i,j).path(k-1),j);" G, L7 h+ `' Z4 @ g
end
! B: P5 u( z( x1 i end
( W' ^# T2 D1 G( }% a3 Cend
+ t0 B+ q7 c- H" o" Ws=sprintf('任意两点之间的最短路径如下:');3 k& n# }2 \" l5 |3 f
disp(s);2 J" S4 F$ o8 \* s7 o) h
for i=1:n
7 [- R" b; y" j2 v- n+ `& J+ s for j=1:n
+ A" J _$ |' z, K2 L s=sprintf('从%d到%d的最短路径长度为:%d\n所经路径为:'...
3 _6 k f W5 K6 a1 ]9 m' S- F ,i,j,Min_path(i,j).distance);
+ d$ ?/ s- Z6 G& [; M disp(s);
- o. q; ~. i9 k5 u disp(Min_path(i,j).path);3 Z: \3 P6 e% A6 ?* H" ^
end
4 d8 \0 q3 C/ _9 ?5 Wend" l# B8 K- v8 U" W2 ~; i- \9 F
5 c, C/ p. T' t) L! e) }9 N# U6 A%找出在指定从start点到terminal点的最短路径,并输出
( O' V6 S2 `; C, pstr1=sprintf('从%d到%d的最短路径长度为:%d\n所经路径为:',...
# b8 w- q! C( n$ ^9 n( r7 w start,terminal,Min_path(start,terminal).distance);
/ i2 y, ~' y" _( T9 Q Bdisp(str1);; `8 f+ L4 e8 V& o' g4 x
disp(Min_path(start,terminal).path);
' g, E& r& Q/ U$ b) S7 ]- P1 @$ i) W6 c: F- a
%Foldy's Algorithm 算法程序
& Z9 X. r) Y; d- V$ ^function [D,path]=floyd1(a)
- |: ?1 _+ i6 A: z* b7 |& |! Z# mn=size(a,1);8 y- n" k: F' C! H$ C; Y9 N
D=a;path=zeros(n,n);%设置D和path的初值
4 x' s W( G5 b8 N4 @" M5 {( Ffor i=1:n, c6 z/ o" f, t& e$ {3 j
for j=1:n' `! q' t$ ]9 b+ I0 t! i: L; h
if D(i,j)~=inf
+ ]! b1 v7 v2 t. d* U( W: z( V/ N path(i,j)=j;%j是i的后点
9 R3 [8 u2 ]3 h: H/ l end
: X8 J' O' e( [6 F2 E2 v# B end, p6 u' p4 ^; f
end I( L: ? K$ [8 I( ?
%做n次迭代,每次迭代都更新D(i,j)和path(i,j)) e; O0 w' b6 }7 ~8 ?
for k=1:n
- d% i+ d/ P2 | @" I" ]: ` for i=1:n
0 b z- ^* a. E- w& O5 w# j for j=1:n( Z+ j! ?+ H+ B1 ^0 y8 O" w
if D(i,k)+D(k,j)<D(i,j)
3 c/ S/ z& @0 [4 F. o( {: L, t D(i,j)=D(i,k)+D(k,j);%修改长度$ m3 N, c1 v- K4 D' B' Y
path(i,j)=path(i,k);%修改路径
" V( `, F+ h" f6 \+ F6 F# Z end! |- N4 X" J1 p/ f, y- n `, p! \
end
. ^# s& g3 c# v1 b* w end
3 w/ G$ e6 U$ u' V5 Qend
+ R x$ v4 e0 q' f$ C
k ~: W1 G( _0 z五 模拟退火算法源程序
* n* i2 q5 L* d% O( ufunction [MinD,BestPath]=MainAneal(CityPosition,pn)
# K% s4 G6 F+ l$ kfunction [MinD,BestPath]=MainAneal2(CityPosition,pn)
3 B6 U0 G1 C/ J3 f, [( h%此题以中国31省会城市的最短旅行路径为例,给出TSP问题的模拟退火程序
% C1 ~# K* V/ J" f%CityPosition_31=[1304 2312;3639 1315;4177 2244;3712 1399;3488 1535;3326 1556;...
2 s. \5 G5 P5 s% 3238 1229;4196 1044;4312 790;4386 570;3007 1970;2562 1756;...
3 z/ A! P0 g7 ^7 S8 z% 2788 1491;2381 1676;1332 695;3715 1678;3918 2179;4061 2370;.... Z9 |: s1 t' \5 [8 d; i2 W
% 3780 2212;3676 2578;4029 2838;4263 2931;3429 1908;3507 2376;...1 t+ P. @2 ~* f
% 3394 2643;3439 3201;2935 3240;3140 3550;2545 2357;2778 2826;2370 2975];3 P- f7 O* O5 y/ P6 P8 @) l0 P
% Z7 Q' x3 ^3 p1 z4 ^% ]: \%T0=clock5 K* [, ?- t: O8 v) u7 w% v
global path p2 D;
S7 a5 l5 @& q[m,n]=size(CityPosition);
) Y6 z. r/ p, F/ S. H2 Z7 B) Z%生成初始解空间,这样可以比逐步分配空间运行快一些9 G( c$ f3 h2 [8 n# o
TracePath=zeros(1e3,m);* _( ?& r9 {* `0 i# T6 ]- w7 \
Distance=inf*zeros(1,1e3);0 T$ j( O8 D) C! F) D3 I; J5 e( l0 ]6 m
( ^/ [. |2 I) W6 b. Y2 P3 ]( ?
D = sqrt((CityPosition( :, ones(1,m)) - CityPosition( :, ones(1,m))').^2 +...) T2 [+ V* n/ x% l G
(CityPosition( : ,2*ones(1,m)) - CityPosition( :,2*ones(1,m))').^2 );
6 A# v' n4 y* L; p- H! i%将城市的坐标矩阵转换为邻接矩阵(城市间距离矩阵)
v- q+ i7 w- A7 Z+ o( Sfor i=1:pn
( e# D3 s3 a& H' H0 q path(i,:)=randperm(m);%构造一个初始可行解
. h x, x$ }7 e9 u8 e) jend/ b: E& B+ b5 R! m$ p- R, e2 B3 t
t=zeros(1,pn);* i* u+ r2 r5 s5 N( f9 Q! c
p2=zeros(1,m);8 X0 K8 g( T9 U3 p; J6 i
% M: D+ c7 `9 M+ i9 Riter_max=100;%input('请输入固定温度下最大迭代次数iter_max=' );
# A) w) D! N6 e- M2 Im_max=5;%input('请输入固定温度下目标函数值允许的最大连续未改进次数m_nax=' ) ; f2 n' D( d' `0 ?' a7 `8 v
%如果考虑到降温初期新解被吸收概率较大,容易陷入局部最优" J: H2 r, G' D7 Z3 n" i
%而随着降温的进行新解被吸收的概率逐渐减少,又难以跳出局限
! W3 I, Q2 t# V* Q. d! X" b! Q%人为的使初期 iter_max,m_max 较小,然后使之随温度降低而逐步增大,可能
; A% z5 d3 |$ b4 n%会收到到比较好的效果
5 H& B2 q( O1 ^! g6 ^) v/ {( G( h1 H: l) e
T=1e5;5 `& V! J, R! a9 q0 o) F5 t1 E5 L
N=1;
R$ u" z# x9 Q/ h; V" v3 Rtau=1e-5;%input('请输入最低温度tau=' );$ q7 n; L4 V: Y, ]
%nn=ceil(log10(tau/T)/log10(0.9));2 q( E* p c3 ^9 R7 [1 r
while T>=tau%&m_num<m_max
* k% k& Q7 r7 A2 u+ i1 q, |, i6 ` iter_num=1;%某固定温度下迭代计数器
* I: R+ c- w) u( l. F0 q m_num=1;%某固定温度下目标函数值连续未改进次数计算器
; h9 R. q9 {% L* T: v. P2 H %iter_max=100;5 P# T( l( T' j9 l- B
%m_max=10;%ceil(10+0.5*nn-0.3*N);
/ ]: ]7 H/ h) o* p while m_num<m_max&iter_num<iter_max
: ]5 \0 k0 q4 O% X7 S* X$ I %MRRTT(Metropolis, Rosenbluth, Rosenbluth, Teller, Teller)过程:
. n* I& I$ p5 P5 R; E %用任意启发式算法在path的领域N(path)中找出新的更优解. F9 i" j# H) N+ ]% L
for i=1:pn1 U3 r/ v# d* M
Len1(i)=sum([D(path(i,1:m-1)+m*(path(i,2:m)-1)) D(path(i,m)+m*(path(i,1)-1))]);1 I4 ^3 [7 G& p! I, p
%计算一次行遍所有城市的总路程 3 [0 l2 n" U1 A7 p) v" _; v
[path2(i,: )]=ChangePath2(path(i,: ),m);%更新路线
r# J- I/ u, z2 O! P2 w Len2(i)=sum([D(path2(i,1:m-1)+m*(path2(i,2:m)-1)) D(path2(i,m)+m*(path2(i,1)-1))]);4 c3 U& j, s! ]+ I7 Y$ d6 \
end
4 f' v; g+ g& Z %Len1* m* O/ V g/ ^2 J" Z
%Len28 A/ t6 p& Y! v' D$ ^- V6 Y2 _: g" I
%if Len2-Len1<0|exp((Len1-Len2)/(T))>rand
4 Z( e: ^1 F' c7 ]; X0 [ R=rand(1,pn);6 J i8 j* M$ m$ I2 y9 O8 n+ V0 T, y
%Len2-Len1<t|exp((Len1-Len2)/(T))>R
4 n' y; }! ^( a# k# z) F- p( U if find((Len2-Len1<t|exp((Len1-Len2)/(T))>R)~=0)% H) U4 d S1 V z7 ]3 A+ ^
path(find((Len2-Len1<t|exp((Len1-Len2)/(T))>R)~=0), : )=path2(find((Len2-Len1<t|exp((Len1-Len2)/(T))>R)~=0), : );* A+ _* M* S3 f6 y0 a- c6 [" e
Len1(find((Len2-Len1<t|exp((Len1-Len2)/(T))>R)~=0))=Len2(find((Len2-Len1<t|exp((Len1-Len2)/(T))>R)~=0));
! F6 j* o( }8 F0 k [TempMinD,TempIndex]=min(Len1);
4 q9 x* ^7 C* e( V. w4 h %TempMinD
+ }1 B# S, f% U4 o- { TracePath(N,: )=path(TempIndex,: );. e) v: m. Q2 @) C' m
Distance(N,: )=TempMinD;8 [" K$ s9 K; i J9 i, D6 u8 s
N=N+1;
, B2 }4 |$ P6 c% f: `) O/ R$ d %T=T*0.9 k7 {& W% V; r E& D j& Z, W3 y' Y
m_num=0;3 l% l6 d3 z9 Z4 M
else
: ?3 ^7 _# Z7 y$ f# B# t/ Y) L m_num=m_num+1;
0 K: ^, y5 j! C! }1 Y+ J$ E end/ H/ o" e, U# S& X( t+ Z$ b
iter_num=iter_num+1;( F- N! v/ w: `
end
# i, m, B, l |$ ?9 w1 E8 p T=T*0.9% k- P* c4 B$ k; l, r: y
%m_num,iter_num,N
% u1 @" a+ P0 w+ dend ' z# o3 f- b4 H% |9 D: ?- ~
[MinD,Index]=min(Distance);
* o/ _0 m, \( qBestPath=TracePath(Index,: );8 m ~/ [6 o$ E* z# D
disp(MinD)
/ C' T( \! Z$ Q; X8 e%T1=clock" X( w) Q- @6 i
. R' V) W( W9 F( W8 P' Q
# M; Q/ P8 [6 v0 N%更新路线子程序 " |2 |1 M! o S- D- T
function [p2]=ChangePath2(p1,CityNum)3 L7 S2 \" k% G- e
global p2;- T9 S' h9 a2 x2 C! l) {5 E& q
while(1)
$ V _* B, h) z4 h8 S R=unidrnd(CityNum,1,2);
/ c( I. N. {1 r5 M* g if abs(R(1)-R(2))>1
6 J! k2 C) b0 f( ^ break;
7 S2 h3 E/ ^- O/ |$ I end+ m0 t" E6 g8 z/ ~) a& B8 n
end G$ L6 m1 L f4 q$ [
R=unidrnd(CityNum,1,2);
( L8 g2 h5 q$ C% j* { B# vI=R(1);J=R(2);
: H/ k& f" i* u& H0 o%len1=D(p(I),p(J))+D(p(I+1),p(J+1));
" A z& x0 _3 f& \/ |) r: x$ D* G%len2=D(p(I),p(I+1))+D(p(J),p(J+1));
( n" O- e& f- ]1 c" }* kif I<J
# B/ J7 _- x, g2 Z p2(1:I)=p1(1:I);/ x; x8 ^) ~' }+ }3 d8 S2 V
p2(I+1:J)=p1(J:-1:I+1);
$ \( d" t! `) Q6 C$ K5 d5 @ p2(J+1:CityNum)=p1(J+1:CityNum);
/ u: p" w0 n! _$ y4 w4 |6 Ielse% e- T P. _ g9 A
p2(1:J)=p1(1:J);
( m+ _9 @# M) _4 h p2(J+1:I)=p1(I:-1:J+1);& Z+ J" p8 [0 l% i# Q: y; {
p2(I+1:CityNum)=p1(I+1:CityNum);, t( m# c! w5 k
end! v3 m7 w8 ^/ }/ _- a
6 y( s6 {! u( r. _0 h* ?六 遗传 算 法程序:5 M; g4 F ]. E9 ~% A0 m# u
说明: 为遗传算法的主程序; 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!9 f0 ^4 ?1 H& M1 }6 B4 C
8 [- M# n, Q3 t: R
function [BestPop,Trace]=fga(FUN,LB,UB,eranum,popsize,pCross,pMutation,pInversion,options)- }# @* n0 I( m
% [BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation) 6 @0 f- [" k- Z/ i, l" \) s! E+ ?
% Finds a maximum of a function of several variables.2 W. p% v* V( f! {) s" x8 u
% fmaxga solves problems of the form: 8 O% c- I) P/ g- A' `
% max F(X) subject to: LB <= X <= UB 7 M: \: F4 W7 o
% BestPop - 最优的群体即为最优的染色体群
" U, @0 o' Y7 D: Q& T! ^4 E( J7 e% Trace - 最佳染色体所对应的目标函数值" ~) N& |; ]% F9 x! z/ V
% FUN - 目标函数7 S: Y0 P/ u) g4 O1 J4 c
% LB - 自变量下限
' @& U+ ? m, K. s; m( s% UB - 自变量上限
$ B! I N; J; {+ H" B8 l% eranum - 种群的代数,取100--1000(默认200)
7 p- ^; y" G) z s2 F0 a% popsize - 每一代种群的规模;此可取50--200(默认100), `' N2 U# Q8 X) Y ~3 D
% pcross - 交叉概率,一般取0.5--0.85之间较好(默认0.8)
2 ~3 |- n( @2 }, e& W0 P% pmutation - 初始变异概率,一般取0.05-0.2之间较好(默认0.1)) Y9 W8 X- \5 ?/ i7 I! c
% pInversion - 倒位概率,一般取0.05-0.3之间较好(默认0.2)# u; y+ @7 U) }4 [
% options - 1*2矩阵,options(1)=0二进制编码(默认0),option(1)~=0十进制编
9 \* k. v! Y8 e8 I%码,option(2)设定求解精度(默认1e-4)
, U' \* K3 H8 c* t' n+ s' [ j%, F: x3 K+ U& ?5 t% Y& k
% ------------------------------------------------------------------------
0 v: |- @1 }7 J/ N& a% Q% S. j
8 v6 P: `$ ^+ B. z" y; fT1=clock;; r# s$ j8 |) s L, B2 t9 ^& x/ Q) S
if nargin<3, error('FMAXGA requires at least three input arguments'); end
. [$ E( Q9 |& K# eif nargin==3, eranum=200;popsize=100;pCross=0.8;pMutation=0.1;pInversion=0.15;options=[0 1e-4];end' F8 i# K! q% j$ ?- d" K
if nargin==4, popsize=100;pCross=0.8;pMutation=0.1;pInversion=0.15;options=[0 1e-4];end$ {! ?: \1 k, f5 a' L, W
if nargin==5, pCross=0.8;pMutation=0.1;pInversion=0.15;options=[0 1e-4];end
, [! Z. N: e8 G! {: Wif nargin==6, pMutation=0.1;pInversion=0.15;options=[0 1e-4];end+ t6 }3 v* `$ N+ Q" |
if nargin==7, pInversion=0.15;options=[0 1e-4];end, v/ K+ ]- C% F6 M! h
if find((LB-UB)>0)
: S) ~1 E/ j$ p5 N3 ]- s5 a error('数据输入错误,请重新输入(LB<UB):');5 q) c9 @8 B! D+ v3 d
end; y3 c, Z: ]+ B; l& G" u, r9 W
s=sprintf('程序运行需要约%.4f 秒钟时间,请稍等......',(eranum*popsize/1000));
- A0 G D2 @1 O% _1 e+ Idisp(s);
+ P! J, S6 O6 r
- U% r% N4 m, v( K7 ]0 Eglobal m n NewPop children1 children2 VarNum1 A* n1 x+ X2 v! N7 L8 U
g: r; ^. b/ f* x6 [bounds=[LB;UB]';bits=[];VarNum=size(bounds,1);- C5 D$ S G+ [0 x; D* a% c
precision=options(2);%由求解精度确定二进制编码长度 ?" S8 I5 B; U5 i% r
bits=ceil(log2((bounds(:,2)-bounds(:,1))' ./ precision));%由设定精度划分区间( o* _7 X6 u, r4 a
[Pop]=InitPopGray(popsize,bits);%初始化种群
) o% ]. D# m6 B% D[m,n]=size(Pop);
3 s. R, A! z' o: v# Q. m- V0 nNewPop=zeros(m,n);/ Z- e. k( @) _" \, o m# Q
children1=zeros(1,n);, ~1 S' m$ P2 _
children2=zeros(1,n);
9 t$ u& S2 _$ L1 Ipm0=pMutation;
! k! |5 V# d% _( FBestPop=zeros(eranum,n);%分配初始解空间BestPop,Trace" T' Z' h3 D$ ^1 o9 Z+ V. E- h! |
Trace=zeros(eranum,length(bits)+1);; J! f& X$ c& Y6 Z, z
i=1;
3 o4 w4 D" b# E, t& B& C" Rwhile i<=eranum
* H6 d* h3 p" Q6 k; \ for j=1:m8 i0 D' _; p. F: z
value(j)=feval(FUN(1,:),(b2f(Pop(j,:),bounds,bits)));%计算适应度' r1 k; H; X5 C& Q0 k O
end5 A% b' G" m& s7 b$ X1 v4 I+ G* t6 o
[MaxValue,Index]=max(value);
* ?7 \9 C8 x8 O" T BestPop(i,:)=Pop(Index,:);. {( L4 f% M4 l
Trace(i,1)=MaxValue;
8 y$ i/ t; C* a# y6 L Trace(i,(2:length(bits)+1))=b2f(BestPop(i,:),bounds,bits); q+ r. O6 F& Z: \2 h! o, D
[selectpop]=NonlinearRankSelect(FUN,Pop,bounds,bits);%非线性排名选择+ c, F& \3 V& F4 g& \! y
[CrossOverPop]=CrossOver(selectpop,pCross,round(unidrnd(eranum-i)/eranum));& E' I6 {$ Y5 m' r! W3 t7 I
%采用多点交叉和均匀交叉,且逐步增大均匀交叉的概率' [' k* ~, u8 ^6 I
%round(unidrnd(eranum-i)/eranum)
' U) q& n D1 F0 M0 e$ l9 G+ Z/ m [MutationPop]=Mutation(CrossOverPop,pMutation,VarNum);%变异
# E- D. d/ j, \ [InversionPop]=Inversion(MutationPop,pInversion);%倒位 G( v" E( }8 ^' G
Pop=InversionPop;%更新0 S3 B [" H' N' u
pMutation=pm0+(i^4)*(pCross/3-pm0)/(eranum^4); & n$ r6 u" Y3 F1 J
%随着种群向前进化,逐步增大变异率至1/2交叉率
5 C( [% B( @# ]0 M; H v! ] p(i)=pMutation;
3 W: C! h+ Q- X' B8 O$ T( k( Q i=i+1;
l5 ^& w$ J( n: m* @end
1 D& i+ e2 t4 Y* n% p2 H" ^2 e4 rt=1:eranum;" G7 {9 D7 q' N8 f+ H6 A0 a3 X
plot(t,Trace(:,1)');
2 C! f5 W+ a9 G8 E, u, ^+ ftitle('函数优化的遗传算法');xlabel('进化世代数(eranum)');ylabel('每一代最优适应度(maxfitness)');
$ N+ f+ [5 F) o* F- g. y6 I[MaxFval,I]=max(Trace(:,1));$ B8 T8 L+ k, i
X=Trace(I,(2:length(bits)+1));1 Y1 f: W0 m5 b1 E6 f* f( ^/ _
hold on; plot(I,MaxFval,'*');* d" C, t- k1 d& f4 ~" L
text(I+5,MaxFval,['FMAX=' num2str(MaxFval)]);2 }" q5 P# [* O0 u+ W/ |/ F+ }$ L
str1=sprintf('进化到 %d 代 ,自变量为 %s 时,得本次求解的最优值 %f\n对应染色体是:%s',I,num2str(X),MaxFval,num2str(BestPop(I,:)));3 `% c, }! U/ o. b1 A" m% z
disp(str1);. y" w6 b: [7 {- ?7 K+ u
%figure(2);plot(t,p);%绘制变异值增大过程
" ~, P) m" [# Y& ?! Y$ @. RT2=clock;+ w" \& K& l! X0 {
elapsed_time=T2-T1;
- b/ v& Q) }% `" jif elapsed_time(6)<0
% n& Y/ ]) p( T) u* f elapsed_time(6)=elapsed_time(6)+60; elapsed_time(5)=elapsed_time(5)-1;
2 J o1 [" z, s. Cend' m/ F1 L% L2 w; h; Q& c3 C' u
if elapsed_time(5)<0
: ?" A( V/ A0 s" {; \0 c6 h2 F4 c elapsed_time(5)=elapsed_time(5)+60;elapsed_time(4)=elapsed_time(4)-1;
* c& s+ }& C' a. a: H& Qend %像这种程序当然不考虑运行上小时啦
2 K+ l$ a' R1 M6 d: v" z& ystr2=sprintf('程序运行耗时 %d 小时 %d 分钟 %.4f 秒',elapsed_time(4),elapsed_time(5),elapsed_time(6));+ [9 `2 w# a* m0 f
disp(str2);
|( r/ L: D1 z! \( ?1 ?3 z/ P7 Z3 U& ~" N* Y1 }
! V) W; M# g1 \0 u5 H6 |1 M; ^. I
%初始化种群
$ U2 p& S; m R( H4 R# R; S, e D%采用二进制Gray编码,其目的是为了克服二进制编码的Hamming悬崖缺点# }0 x0 D4 }9 i) ^
function [initpop]=InitPopGray(popsize,bits)
: b. V" I" q( v7 M1 v8 Z, Hlen=sum(bits);9 r1 l- p; `4 [- u* Y
initpop=zeros(popsize,len);%The whole zero encoding individual/ q( F0 i" n4 {8 m; }8 T U, y
for i=2:popsize-1
' g* ~; H) r1 L8 P$ N pop=round(rand(1,len));
8 ]0 q: u/ K: [4 Z1 v" m pop=mod(([0 pop]+[pop 0]),2);
( O& U. Q/ |6 v3 d3 f& m6 R" C %i=1时,b(1)=a(1);i>1时,b(i)=mod(a(i-1)+a(i),2)
3 w! J- b7 K1 H% O! T: L %其中原二进制串:a(1)a(2)...a(n),Gray串:b(1)b(2)...b(n)3 O% u; A3 L" Q, i' f0 `
initpop(i,:)=pop(1:end-1);
+ C6 d* z1 D0 }5 F+ Jend
1 K, t1 F. J: M& Winitpop(popsize,:)=ones(1,len);%The whole one encoding individual
) \1 N7 \' r$ o2 y" |$ e%解码6 T- F4 \0 }& H: F$ h
6 Y- z' G {2 q6 i- qfunction [fval] = b2f(bval,bounds,bits)
, m' ?1 [; t' Y% ]8 M) u* u% fval - 表征各变量的十进制数, |8 {1 y) |+ G5 M c1 L% A
% bval - 表征各变量的二进制编码串+ L! i' R- p% H. h
% bounds - 各变量的取值范围
+ O- L1 Z0 t" j( x% bits - 各变量的二进制编码长度
5 f) K: k/ i& V# {% q! ?scale=(bounds(:,2)-bounds(:,1))'./(2.^bits-1); %The range of the variables
0 P1 T, F; W( {7 s/ H% k: hnumV=size(bounds,1);/ K8 y9 O& ^+ w6 F' X
cs=[0 cumsum(bits)];
6 V; v3 l ]+ C5 s4 X& Wfor i=1:numV
5 \# |( M. F$ y a=bval((cs(i)+1):cs(i+1));/ a7 J' T+ [4 v( O {( ]7 b
fval(i)=sum(2.^(size(a,2)-1:-1:0).*a)*scale(i)+bounds(i,1);- M0 B% |, p; ?) ^9 x2 \
end$ _! a' R7 ^) f" P' X6 T0 W
%选择操作
2 \; H3 A% V+ G! d, n }%采用基于轮盘赌法的非线性排名选择# D5 B* h2 f3 T/ v% r" \0 Q
%各个体成员按适应值从大到小分配选择概率:; q! L# E3 ^6 ]. C) r
%P(i)=(q/1-(1-q)^n)*(1-q)^i, 其中 P(0)>P(1)>...>P(n), sum(P(i))=1
, [: P8 L4 S# J# J. x6 _7 |/ Q" W; s- p, x1 e( s! }. _ ]: k
function [selectpop]=NonlinearRankSelect(FUN,pop,bounds,bits)) E) b+ g2 P- \' W( O
global m n
7 ]7 O# Q1 [+ b+ rselectpop=zeros(m,n);0 s) W% Q, {7 D0 {7 x; N( q
fit=zeros(m,1);% D% \, E" p0 v
for i=1:m3 U8 m0 V5 ?; ^5 p+ f, K
fit(i)=feval(FUN(1,:),(b2f(pop(i,:),bounds,bits)));%以函数值为适应值做排名依据
7 n6 f+ w7 ?# |end
4 B5 t# M( P! C4 c0 X7 M$ @- P$ Q2 qselectprob=fit/sum(fit);%计算各个体相对适应度(0,1) @( [* a. V$ _; R# f
q=max(selectprob);%选择最优的概率- e( d, z& }8 j* d S; f
x=zeros(m,2);
5 A" {8 w* ^. J6 }* H/ Ox(:,1)=[m:-1:1]';. M% W' R% Q# k0 Z9 n ]% J
[y x(:,2)]=sort(selectprob);
! e- U/ }1 \8 n9 Ar=q/(1-(1-q)^m);%标准分布基值
* G* M5 x8 m) u9 g) M1 \newfit(x(:,2))=r*(1-q).^(x(:,1)-1);%生成选择概率( F# s9 O, Q: F6 t, a" j
newfit=cumsum(newfit);%计算各选择概率之和7 Z `0 X- k R, N9 X
rNums=sort(rand(m,1));8 h9 G$ r! g* H0 Y
fitIn=1;newIn=1;8 Y" h% s' z0 ]
while newIn<=m
5 K9 D5 A4 a2 q5 P if rNums(newIn)<newfit(fitIn)
2 F; H/ y/ O3 C, H i# {9 C, \ selectpop(newIn,:)=pop(fitIn,:);
5 c' _! [ M6 l8 k. C7 [& u newIn=newIn+1; s5 I7 J. a6 I5 Z! D4 K
else5 k2 n, z" J/ x/ p8 _- j# m3 x
fitIn=fitIn+1;
; T$ _. }# S) V2 V( q4 d9 U end+ j1 h2 |" R. @2 {5 u
end1 T# ]0 @( a2 a! h
%交叉操作
/ u0 K2 m) A, f* n, T+ U9 wfunction [NewPop]=CrossOver(OldPop,pCross,opts)3 s3 ]7 w# Z& e: ]3 r) j0 V( ]
%OldPop为父代种群,pcross为交叉概率
- c- Y, v5 c" s& @global m n NewPop
: B3 O8 @1 M4 X! X6 W) z0 wr=rand(1,m);# G6 }, t" c3 i( Z) }
y1=find(r<pCross);
% \* R7 P6 e& L" z$ Wy2=find(r>=pCross); y* m, M4 Q. ~2 ~ s: [
len=length(y1);- S3 A$ x4 R. S- _" F
if len>2&mod(len,2)==1%如果用来进行交叉的染色体的条数为奇数,将其调整为偶数
2 k( p$ q5 X& q* w y2(length(y2)+1)=y1(len);' H4 U1 g/ `1 r
y1(len)=[];
0 U. h0 T- v; L2 |% Zend, _2 {2 z! E# S5 o/ Y
if length(y1)>=2
9 u- f# p. d: |# z: _) v# W0 r" F2 k for i=0:2:length(y1)-2/ s5 L) r. v7 O3 V& k! }" l2 ~/ f
if opts==0; Q }# { [+ i$ p. W. @: U
[NewPop(y1(i+1),:),NewPop(y1(i+2),:)]=EqualCrossOver(OldPop(y1(i+1),:),OldPop(y1(i+2),:));
/ M r) _% _/ B t else- Z# @% y) u% M# E% `
[NewPop(y1(i+1),:),NewPop(y1(i+2),:)]=MultiPointCross(OldPop(y1(i+1),:),OldPop(y1(i+2),:));3 L& i0 x! \3 t0 S$ A& m# U. n
end
; F+ }1 e& n( g: b; P end
L- D; Y7 \5 w) p& m) @* L: Dend
" S3 X" a. O: [: g |5 nNewPop(y2,:)=OldPop(y2,:);
! g! o+ W. _+ I6 r: O
- k: O) ~3 t$ g2 W%采用均匀交叉 ' n6 b9 M4 l1 P, T9 r; `. Y
function [children1,children2]=EqualCrossOver(parent1,parent2)' H4 J& I4 J* l X5 x% n0 L/ q
1 Y! F# M* S! x
global n children1 children2
/ @# N" h8 w# ` p0 x! [/ J1 d; ^hidecode=round(rand(1,n));%随机生成掩码
0 |$ Y, y& v- e) Zcrossposition=find(hidecode==1);
: o: W ]* k. z- Q/ Hholdposition=find(hidecode==0);
; q% W7 D1 J1 E* v* Pchildren1(crossposition)=parent1(crossposition);%掩码为1,父1为子1提供基因 K( \; i! T' c7 c% e
children1(holdposition)=parent2(holdposition);%掩码为0,父2为子1提供基因
: n/ w ~: _7 K1 x9 Qchildren2(crossposition)=parent2(crossposition);%掩码为1,父2为子2提供基因; K8 c% I6 S* ?% l9 a0 O
children2(holdposition)=parent1(holdposition);%掩码为0,父1为子2提供基因; z! X- ^1 `- ?' F+ N
- J) H2 j5 U% z/ V) g7 \. L$ s- ]
%采用多点交叉,交叉点数由变量数决定
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function [Children1,Children2]=MultiPointCross(Parent1,Parent2). F9 b6 Q+ G1 }% v1 D3 Y
: C. U% `4 ~& O$ T- Z- Rglobal n Children1 Children2 VarNum
+ h/ ]8 `0 X3 `- c# Z4 lChildren1=Parent1;
5 O4 G1 k k9 r; Y& s& dChildren2=Parent2;. F) K9 e0 @ u3 q$ V6 X
Points=sort(unidrnd(n,1,2*VarNum));* y9 h. `! _7 T* Q L& B! U; M
for i=1:VarNum. M T) V- }3 e( y
Children1(Points(2*i-1):Points(2*i))=Parent2(Points(2*i-1):Points(2*i));
/ r! h& N4 r8 L+ P: \ Children2(Points(2*i-1):Points(2*i))=Parent1(Points(2*i-1):Points(2*i));4 f: I9 j# @5 t& A# V: n+ {+ {1 d
end. P9 L. `! x1 t0 x$ c# g
' \, r% O& y: q. z4 l8 h% H
%变异操作$ q! m9 X$ [7 r5 b
function [NewPop]=Mutation(OldPop,pMutation,VarNum)
. ^; {0 e" p5 r1 E( m& h" G' C- Z) U' O/ n6 ]/ Y9 U1 s: W u( E
global m n NewPop0 y- Q) S( i% M7 J7 O! ^. {
r=rand(1,m);
F9 r' p r1 v+ e A& {0 n, Nposition=find(r<=pMutation);
6 x' i# }( z$ ^( w! |len=length(position);
% H* B( M0 @3 [# A+ H! z- P. D3 ]if len>=1
( a7 j" @3 z- a x) n1 u* G; X' J for i=1:len
3 Z3 Q3 G5 f! l k=unidrnd(n,1,VarNum); %设置变异点数,一般设置1点
& T% S8 f" I8 V3 D for j=1:length(k)
: q( H0 h7 u7 k* n3 I4 j if OldPop(position(i),k(j))==1
) r [; p- [/ V0 I OldPop(position(i),k(j))=0;
4 T. A& _1 g9 _. F else
. s" O6 s) ^- u OldPop(position(i),k(j))=1;# D. {+ Y; y# l u& L ?
end' N+ J( ^6 t& M$ ^9 l, G. N
end
7 V" f7 `9 n0 Z! [ end
! C. S; @6 r/ O& ^5 T4 ?% F5 lend
$ }/ X5 z, X6 x( O) qNewPop=OldPop;
) x, v, S' p7 N1 s& X
& ~9 V- t6 y2 S%倒位操作
' f+ N* O# t8 ?( q. H" E$ ^" N- h0 D! |& P# P$ ^: a2 ~
function [NewPop]=Inversion(OldPop,pInversion)
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global m n NewPop
9 w" B: U' V9 d. sNewPop=OldPop;; Y8 g, x- x# x7 H; @% Q- @0 j: V& ? L
r=rand(1,m);
1 M0 P" ] j0 \/ [" u. `PopIn=find(r<=pInversion);/ O1 L# e1 X) V( |2 ? o/ }4 B& ^
len=length(PopIn);
) z# o; n7 M" Uif len>=19 S7 R* |& x8 _2 B, e* V9 k! C
for i=1:len
% P; S9 O, j/ Y2 _+ L y d=sort(unidrnd(n,1,2));
) y* n$ d2 u( E y if d(1)~=1&d(2)~=n
$ A4 f* E0 G* b& g/ A) l NewPop(PopIn(i),1:d(1)-1)=OldPop(PopIn(i),1:d(1)-1);
4 |9 v; x1 \/ d1 y' N; r P Q9 Y NewPop(PopIn(i),d(1):d(2))=OldPop(PopIn(i),d(2):-1:d(1));
4 E& b$ n$ N9 p" A3 g; l% u) K NewPop(PopIn(i),d(2)+1:n)=OldPop(PopIn(i),d(2)+1:n);
+ C$ X" w0 r4 X( |5 U0 \ end
$ x' {1 G1 w0 [3 ]; |# K end9 J9 l# |: y$ B8 x" v8 K4 [
end# l. u: c, R! ^
, Y5 o; [9 E% M% y七 径向基神经网络训练程序
* q1 Y4 }1 Z$ L' ?" a9 V6 C8 H; s
7 _2 D. L( q/ ^, `. y& S7 Wclear all;) J. w/ D8 T! n$ F6 g
clc;
3 I3 M; ^. g% D; C+ q. h/ n' Q%newrb 建立一个径向基函数神经网络 {( K2 J; N. I% Q
p=0:0.1:1; %输入矢量8 U5 T- V/ u0 V+ Q; S) {
t=[0 -1 0 1 1 0 -1 0 0 1 1 ];%目标矢量# K- q& ?* s: W/ o
goal=0.01; %误差
" q3 X6 d7 Q& B! R, `. O9 Fsp=1; %扩展常数4 y9 W3 X' P$ q$ N( R
mn=100;%神经元的最多个数! Y' s& T! U4 X9 O+ c
df=1; %训练过程的显示频率
6 I: l' P8 Q6 s+ P: c[net,tr]=newrb(p,t,goal,sp,mn,df); %创建一个径向基函数网络5 l+ K; g. d9 }" _( B! N
% [net,tr]=train(net,p); %调用traingdm算法训练网络
2 f0 Z: T& Z' m( R" q Z: g/ |7 i%对网络进行仿真,并绘制样本数据和网络输出图形
! {' N& }0 \' S9 q/ MA=sim(net,p);
" O# k+ a5 ]5 Y( D mE=t-A;
7 G4 A& C2 H/ |sse=sse(E);* K) h8 w5 a3 l5 v( [' W; F+ }. T
figure; 0 Z& N: E9 [1 y: a/ P! g/ X
plot(p,t,'r-+',p,A,'b-*');
% o4 R/ W# p( u* n( Clegend('输入数据曲线','训练输出曲线');3 N6 v) B; ?! O1 ^& d1 a1 K
echo off
8 {0 B- i0 a$ o
5 v: v2 |; e1 b4 U. u说明:newrb函数本来 在创建新的网络的时候就进行了训练!
) n* g2 |7 g: h2 e5 ]+ O, L每次训练都增加一个神经元,都能最大程度得降低误差,如果未达到精度要求,
& a3 u* T/ m1 Z! M0 `1 J& |+ T那么继续增加神经元,程序终止条件是满足精度要求或者达到最大神经元的数目.关键的一个常数是spread(即散布常数的设置,扩展常数的设置).不能对创建的net调用train函数进行训练!9 M; @# I# \2 F6 v+ A. X2 I# E" X) ^
3 b% L/ x" r, x9 [1 ?- U. P
6 E- q1 K" n) P$ q3 a6 a训练结果显示:& E+ t+ H2 a8 n7 j% W$ X# s/ x
NEWRB, neurons = 0, SSE = 5.0973' E$ M- c9 q& J% ]+ [4 T0 G0 f& Y1 l
NEWRB, neurons = 2, SSE = 4.87139
9 x3 a+ s, m3 G# n+ S% FNEWRB, neurons = 3, SSE = 3.61176- x6 b$ m0 V) Z
NEWRB, neurons = 4, SSE = 3.4875
5 o! A3 Y4 q* I- q- pNEWRB, neurons = 5, SSE = 0.534217+ X) u6 s3 c4 E
NEWRB, neurons = 6, SSE = 0.51785; R, O4 @1 S) x2 s6 I. h) L! l
NEWRB, neurons = 7, SSE = 0.4342598 [2 G& A! c0 }1 }
NEWRB, neurons = 8, SSE = 0.341518
: K/ g$ r7 h+ v% h# O! }NEWRB, neurons = 9, SSE = 0.3415196 p- [$ f! e5 Z# G
NEWRB, neurons = 10, SSE = 0.002578320 P- s, W: ^* _, |) d+ q* l
; n% Q: @: |- p( R v
八 删除当前路径下所有的带后缀.asv的文件$ m3 d8 F. K9 c3 F& O
说明:该程序具有很好的移植性,用户可以根据自己地
1 P$ \" @- A3 F要求修改程序,删除不同后缀类型的文件!
6 j' @$ S3 {# a5 b# r: F& Gfunction delete_asv(bpath)
8 ?0 B) N7 U8 n" J- ?0 ~( J" G) U%If bpath is not specified,it lists all the asv files in the current& q2 q! F9 q- j; S; O# s
%directory and will delete all the file with asv
f7 {7 ?- [- H6 E% Example:
, P0 w' V: [+ I5 | Y: U: ~1 k: t% delete_asv('*.asv') will delete the file with name *.asv;1 {7 b1 T p$ |7 s6 O0 z: @$ _2 n% j1 l
% delete_asv will delete all the file with .asv.! ]7 y8 j8 v+ D6 L! I
2 B* }% i: S$ ~5 e
if nargin < 13 Q! O6 P) K' x& q$ |+ k6 X) E4 I, l* t8 [
%list all the asv file in the current directory
; s- i0 j: k g/ t: l2 p4 n5 F# z files=dir('*.asv');, ^) |# o0 Q# a
else3 Y( n, }+ s1 B, q- q! I0 E
% find the exact file in the path of bpath
/ d) ?' ]. @, M* T1 L/ ~ [pathstr,name] = fileparts(bpath);
" H2 T- q3 F' {# n2 a if exist(bpath,'dir')
( s2 `& y9 p7 Q name = [name '\*'];
) w6 Q V0 \+ T/ o) i end: h% h5 R3 h* o# @' F r( P: W" l( F
ext = '.asv';
" Z$ F) Q) G4 P q& m/ f5 {! i files=dir(fullfile(pathstr,[name ext]));5 {; W2 r, x1 V3 n
end
& i1 ?2 g: I/ B, J U Q# F6 n# ~: l- p' B# Y
if ~isempty(files)
9 J# h& R# G1 ^1 v( p for i=1:size(files,1)
; p2 _0 B! [3 i" J title=files(i).name;( ~! h$ u4 }9 n
delete(title);9 w, y6 E6 f5 ~7 X7 h7 Q
end
) G" G& K$ c0 t6 N3 vend
' q6 j# }* w$ F4 O B+ P# E4 y1 c# w3 ~
6 u r1 f* [0 L* r% Y( a2 ^, ]同样也可以在Matlab的窗口设置中取消保存.asv文件!
# @- A8 L. I8 z9 X% t1 U0 q& I3 x |
zan
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