隐马尔科夫分类 opt opt ,迭代过程 A→5 opt 。把多阶段过程转化为一系列单阶段问题 A→S 。迭代计算A到各阶段、不同状态的最优路径 opt 43s=A{s-"y→s =ga(e】 动态规划可以得到A到各阶段不同状 态的最优路径
迭代过程 ◦ 把多阶段过程转化为一系列单阶段问题 ◦ 迭代计算A到各阶段、不同状态的最优路径 [ 1] * [ 1] [ ] [ 1] [ ] * [ 1] [ 1] [ ] arg max , k opt opt k k k i k k k s opt k A s A s s s f d A s s s [2] opt A s i [3] opt A s i [4] opt A s i ... ... [ ] opt K A s i 动态规划可以得到A到各阶段不同状 态的最优路径
clc 隐马尔科夫分类 %初始化网络 Nstage 5; Nstate(1)=3; Nstate(2)=3: Nstate(3)=3; Nstate(4)=3: Nstate(5)=3; for iii 1:(Nstage-1) Ddistfiii)=round(rand(Nstate(iii),Nstate(iii +1))*9)+ 1: end %初步结果 (3.1) (32) (3.3) (3,4) (35) A=3:%起始节点 for iii =1:Nstate(1) resultfiii,1).WA inf: resultfiii,1).RA [] end result(A,1).WA =0; 10 result(A,1).RA A; for iii=1±Nstate(2) 9 resultfiii,2).WA Ddist(1KA,iii); resultfiii,2).RA [A.iii]: 21)】 22 23 (24) (2.5 end for iii =3:Nstage for jij 1 Nstate(iii) for kkk 1:Nstate(iii -1) tmp(kkk)=result[kkk,iii-1).WA Ddistfiii- 1K(kkk,jjj); end [aa,bb]max(tmp); 1,1 (15) resultfjj.iii.WA aa: for kkk 1:(iii -1) result(jjj,iii).RA(kkk)=result(bb,iii 1).RA(kkk); end resultijj,iii).RA(iii)=j: 0.5 1.5 2.5 3.5 end end %显示 %初始化节点位置 for iii =1 Nstage for jij 1 Nstate(iii) P,iii=[ii-1,i训-1： end end figure;hold on; CCMap =['k','m','b','r','c','g','y]: axis-0.3,4.3,-0.3,2.3JD
0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 (1,1) (2,1) (3,1) (1,2) (2,2) (3,2) (1,3) (2,3) (3,3) (1,4) (2,4) (3,4) (1,5) (2,5) (3,5) 7 7 8 3 9 2 10 8 8 2 10 4 6 3 3 9 1 5 3 6 8 1 7 5 2 5 9 3 2 7 7 1 3 8 7 1 8 9 8 close all clc % 初始化网络 Nstage = 5; Nstate(1) = 3; Nstate(2) = 3; Nstate(3) = 3; Nstate(4) = 3; Nstate(5) = 3; for iii = 1 : (Nstage - 1) Ddist{iii} = round(rand(Nstate(iii), Nstate(iii + 1)) * 9) + 1; end % 初步结果 A = 3; % 起始节点 for iii = 1 : Nstate(1) result{iii, 1}.WA = inf; result{iii, 1}.RA = []; end result{A, 1}.WA = 0; result{A, 1}.RA = A; for iii = 1 : Nstate(2) result{iii, 2}.WA = Ddist{1}(A, iii); result{iii, 2}.RA = [A, iii]; end for iii = 3 : Nstage iii for jjj = 1 : Nstate(iii) for kkk = 1 : Nstate(iii - 1) tmp(kkk) = result{kkk, iii - 1}.WA + Ddist{iii - 1}(kkk, jjj); end [aa, bb] = max(tmp); result{jjj, iii}.WA = aa; for kkk = 1 : (iii - 1) result{jjj, iii}.RA(kkk) = result{bb, iii - 1}.RA(kkk); end result{jjj, iii}.RA(iii) = jjj; end end % 显示 % 初始化节点位置 for iii = 1 : Nstage for jjj = 1 : Nstate(iii) P{jjj, iii} = [iii - 1, jjj - 1]; end end figure; hold on; CCMap = ['k', 'm', 'b', 'r', 'c', 'g', 'y']; axis([-0.3, 4.3, -0.3, 2.3]) for iii = 1 : (Nstage)