蚁群算法的改进神经网络程序.doc

%蚁群算法的改进神经网络程序%这是某个硕士论文中附带的程序，经过试用，完全不能用。 %蚁群算法优化神经网络%参数值 %ant_amount，蚂蚁总数%quanzhi_amount，权值总数%xuanzhi_amount，可选值总数%k，蚂蚁k%i，权值j%j，选值j%tao，信息素矩阵%zeng_tao，信息素增量%NC，最大迭代%rou，信息挥发度;%Q，信息总量:为蚂蚁循环一周时释放在所经过的路径上的信息素总量%zuixiaowucha，最小误差%shijiwucha实际误差%conter迭代次数%sum_tao，tao和%p，信息素概率%4个矩阵:% iwb(i,j)，权值矩阵(i*j)% tao(i,j)，信息素矩阵(i*j)%mayi_xuanzhi(k,i)，每只蚂蚁选的权值矩阵(k*i)%mayi_xuanwei(k*i)，每只蚂蚁选的权值位置矩阵(k*i)%网络主程序Clear;yangben_yuanshi= 7.8 111 701 129 2.8; 45 168 330 82 3.1; 58 103 251 51 6; 56 144 164 36 1.8; 82 161 406 79 8.9; 10 15 19.5 5 0; 18 135 208 59 0; 129 181 244 44 0; 12 23 98 9 0; 164 244 497 103 8.3; 16 237 470 92 0; 15 125 574 29 7; 49 41.6 124 25.1 15.7; 21 7.6 96.3 4.7 26.73; 65 26.1 41.6 10.1 57.8; 64 22 51.7 20.7 95.1; 29 7.52 14.8 5.5 5.4; 6.3 7.87 3.03 6.97 11.1; 413 6.8 4.6 40.4 8.15; 23.4 14.3 9.71 7.12 3.44; 15.2 2.4 4.5 4.8 27.2; 45 431 517 210 2; 65 150 54 45 0; 4 70.7 211 76 18.9; 22 12 6.3 4.6 7.4; 130.4 48.8 93.4 16.3 123.4; 198 54.3 107.1 6.4 148.8; 521.1 107 177.1 9.2 144.7; 217.5 40 51.8 4.9 67.5; 63 7.4 17 1.8 6.2; 54 7 8.6 7.4 5.4; 150 27 63 3.6 90; 162 109 201 11 183; 59 28 70 9 15; 201 36.1 32.3 6 47.2; %训练样本初值for i=1:35 % 训练样本归一化yangben_guiyi(i,:)=0.1+0.8*(yangben_yuanshi(i,:)-min(yangben_yuanshi(i,:)/(max(yangben_yuanshi(i,:)-min(yangben_yuanshi(i,:);endyangben_shuchu= 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 1 0 0 0 0; 0 1 0 0 0; 0 1 0 0 0; 0 1 0 0 0; 0 1 0 0 0; 0 0 1 0 0; 0 0 1 0 0; 0 0 1 0 0; 0 0 1 0 0; 0 0 1 0 0; 0 0 0 1 0; 0 0 0 1 0; 0 0 0 1 0; 0 0 0 1 0; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; 0 0 0 0 1; % 训练样本故障输出值yangben_guiyi=yangben_guiyi;yangben_shuchu=yangben_shuchu;net=newff(minmax(yangben_guiyi),11,5,tansig,logsig,traingd);%生成原始神经网络XX=yangben_guiyi;YY=yangben_shuchu;P=XX;T=YY;R=size(P,1);S2=size(T,1);S1=11; %隐含层节点数S=R*S1+S1*S2+S1+S2; %蚁群算法编码长度% start 初始化ant_amount=40; %蚂蚁总数quanzhi_amount=S; %权值总数xuanzhi_amount=30; %可选值总数rand(state,0);tao=rand(quanzhi_amount,xuanzhi_amount); %tao,信息素矩阵NC=1000; % 最大迭代次数rou=0.7; % 信息持久性Q=30; % 信息总量p=zeros(xuanzhi_amount,1); % p，信息素概率zuixiaowucha=0.001; % 最小误差counter=1; % 迭代计数shijiwucha=0.1; % 实际误差 iwb=rand(S,xuanzhi_amount)*(-20)+10; % 权值矩阵mayi_xuanzhi=zeros(ant_amount,quanzhi_amount); % 每只蚂蚁选的权值矩阵（k*i）初始化mayi_xuanwei=zeros(ant_amount,quanzhi_amount); % 每只蚂蚁选的权值位置矩阵（k*i）初始化while shijiwuchazuixiaowucha if counterNC break; end % 蚂蚁选参 for k=1:ant_amount % 蚂蚁 k,ant_amount:蚂蚁总数 for i=1:quanzhi_amount sum_tao=0; for j=1:xuanzhi_amount sum_tao=sum_tao+tao(i,j); end for j=1:xuanzhi_amount p(j,1)=tao(i,j)/sum_tao; end % 选k rand(state,0); random=rand(1); sum_p=p(1,1); j=0; while sum_p=xuanzhi_amount break; end j=j+1; sum_p=sum_p+p(j,1); end taowei=j; mayi_xuanzhi(k,i)=iwb(i,taowei); mayi_xuanwei(k,i)=taowei; end end P=XX; T=YY; R=size(P,1); S2=size(T,1); S1=11; %隐含层节点数 S=R*S1+S1*S2+S1+S2; %蚁群算法编码长度%前R*S1个编码为Wl for i=1:S1 for k=1:R W1(i,k)=iwb(R*(i-1)+k); end end%接着的S1*S2个编码（即第R*S1个后的编码）为W2 for i=1:S2 for k=1:S1 W2(i,k)=iwb(S1*(i-1)+k+R*S1); end end% 接着的S1个编码（即第R*S1+S1*S2个后的编码）为B1 for i=1:S1 B1(i,1)=iwb(R*S1+S1*S2)+i); end% 接着的S2个编码（即第R*S1+S1*S2+S1个后的编码）为B2 for i=1:S2 B2(i,1)=iwb(R*S1+S1*S2+S1)+i); end net.IW1,1=W1; %权值iw net.LW2,1=W2; %权值lw net.b1,1=B1; %权值b net.b2,1=B2; %权值b XX=P; YY=T; o=sim(net,XX); e=abs(o-YY); shijiwucha=max(e); shijiwucha=max(shijiwucha); zeng_tao=zeros(quanzhi_amount,xuanzhi_amount); zeng_tao=zeng_tao+Q/shijiwucha; tao=rou*tao+zeng_tao; counter=counter+1;endP=XX;T=YY;R=size(P,1);S2=size(T,1);S1=11;S=R*S1+S1*S2+S1+S2;for i=1:S1 for k=1:R W1(i,k)=iwb(R*(i-1)+k); endendfor i=1:S2 for k=1:S1 W2(i,k)=iwb(S1*(i-1)+k+R*S1); endendfor i=1:S1 B1(i,1)=iwb(R*S1+S1*S2)+i);endfor i=1:S2 B2(i,1)=iwb(R*S1+S1*S2+S1)+i);endnet.IW1,1=W1;net.LW2,1=W2;net.b1,1=B1;net.b2,1=B2;XX=P;YY=T;yangbenx_yuanshi=192 45 51 14 0; 70.4 69.5 241.2 28.9 10.4; 98 123 296 33 16; 205 370 535 26 805; 448 93 145 7 637; 125 390 497 202 0.69; 160 90 17 27 5; 437 113 84 9 90; 360 100 230 16 380; % 故障辨识样本初值for i=1:9 %故障样本归一化yangbenx_guiyi(i,:)=0.1+0.8*(yangbenx