pt.m

用matlab编的神经网络预测方面的界面

p=[0.4942,0.75,0.5,0.4017,0.1601,0.9314,0.9314,0.3974,0.6654,0.1111;
    0.4798,0.75,0.5,0.4017,0.1601,0.9375,0.9375,0.3974,0.6554,0.2222;
    0.4817,0.75,0.5,0.4017,0.1601,0.9375,0.9375,0.9374,0.6554,0.4444;
    0.5418,0.75,0.5,0.4017,0.1601,0.9036,0.9036,0.3974,0.4912,0.4444;
    0.5416,0.75,0.5,0.4017,0.1601,0.9036,0.9036,0.3974,0.4912,0.7778;
    0.4278,1,0.75,	0.405594406,0.092678869	,1	,1	,0.509286413,	0.855368421	,0;
    0.689872091	1	0.75	0.405594406	0.277870216	0.982764236	0.982754439	0.509286413	0.855368421	0;
    0.616481443	1	0.75	0.405594406	0.06156406	1	1	0.509286413	0.855368421	0;
    0.643740826	1	0.75	0.811188811	0.277870216	1	1	0.509286413	0.855368421	0
    0.705179283	0.75	0.75	0.965034965	0.183028286	0.828918083	0.881827884	0.614369501	0.699157895	0.555555556;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.785740274	0.614369501	0.699157895	0.555555556;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.81848501	0.614369501	0.699157895	0.555555556;
     0.764940239	0.75	0.75	0.965034965	0.183028286	0.813148038	0.865055302	0.614369501	0.699157895	0.555555556;
     0.705179283	0.75	0.75	0.965034965	0.183028286	0.828918083	0.881827884	0.614369501	0.699157895	0.777777778;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.785740274	0.614369501	0.699157895	0.777777778;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.81848501	0.614369501	0.699157895	0.777777778;
     0.764940239	0.75	0.75	0.965034965	0.183028286	0.813148038	0.865055302	0.614369501	0.699157895	0.777777778;
     0.705179283	0.75	0.75	0.965034965	0.183028286	0.828918083	0.881827884	0.614369501	0.699157895	1;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.785740274	0.614369501	0.699157895	1;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.81848501	0.614369501	0.699157895	1;
     0.702033969	0.75	0.75	0.965034965	0.183028286	0.813148038	0.865055302	0.614369501	0.699157895	1;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.785740274	0.614369501	0.699157895	1.111111111;
     1	0.75	0.75	0.965034965	0.183028286	0.769380055	0.81848501	0.614369501	0.699157895	1.111111111;
     0.702033969	0.75	0.75	0.965034965	0.183028286	0.813148038	0.865055302	0.614369501	0.699157895	1.111111111;
     0.555881736	0.75	0.75	0.426573427	0.183028286	0.868709625	0.924250509	0.614369501	0.699157895	0;
     0.429859509	0.5	0.5	0.412587413	0.199667221	0.914187612	0.926421364	1	1	0;
     0.429859509	0.5	0.5	0.412587413	0.399334443	0.914187612	0.926421364	1	1	0;
     0.429859509	0.5	0.5	0.412587413	0.602329451	0.914187612	0.926421364	1	1	0;
     0.429859509	0.5	0.5	0.412587413	1	0.914187612	0.926421364	1	1	0;
     0.297756343	0.5	0.5	0.643356643	0.199667221	0.870623202	0.839017173	0.657869013	1	0;
     0.297756343	0.5	0.5	0.643356643	0.399334443	0.870623202	0.839017173	0.657869013	1	0;
     0.845040889	0.75	0.625	1	0.245923461	0.830058086	0.786261764	0.782013685	0.657894737	0;
     0.654225204	1	1	0.444965035	0.18469218	0.827533793	0.972433783	0.631476051	0.643368421	0.333333333;
     0.788425246	0.6	0.45	0.731678322	0.068219634	0.876920363	0.820946929	0.631476051	0.421052632	0;
     0.788425246	0.6	0.45	0.731678322	0.10249584	0.876920363	0.820946929	0.631476051	0.421052632	0;
     0.788425246	0.6	0.45	0.731678322	0.10249584	0.876920363	0.820946929	0.631476051	0.421052632	0.555555556;
     0.788425246	0.6	0.45	0.731678322	0.204991681	0.876920363	0.820946929	0.631476051	0.421052632	0;
     0.788425246	0.6	0.45	0.731678322	0.204991681	0.876920363	0.820946929	0.631476051	0.421052632	0.777777778]
 t=[0.133832335;
0.137724551;
0.134730539;
0.125748503;
0.144086826;
0.449101796;
0.616766467;
0.422904192;
0.844311377;
0.95508982;
0.909431138;
0.97754491;
0.863772455;
0.863772455;
1;
0.97754491;
0.932634731;
0.95508982;
0.932634731;
0.909431138;
0.97754491;
0.97754491;
0.931886228;
0.909431138;
0.5;
0.155314371;
0.177769461;
0.188997006;
0.254491018;
0.164670659;
0.202095808;
0.628742515;
0.799850299;
0.168413174;
0.174026946;
0.25261976;
0.249812874;
0.308757485]
%addpath d:\MATLAB6.5\toolbox\LS_SVMlab
%type = 'function approximation';
%kernel = 'RBF_kernel';
%gam = 10;
%sig2 = 0.2;
%model = initlssvm(p,t,type,gam,sig2,kernel);
%model = trainlssvm(model);
%x=[0.450828266	0.9	0.65	0.384615385	0.070998336	0.950002714	0.848937615	0.384066471	0.662736842	0;
%   0.450828266	0.9	0.65	0.384615385	0.141946755	0.950002714	0.848937615	0.384066471	0.662736842	0;
%   0.450828266	0.9	0.65	0.384615385	0.212828619	0.950002714	0.848937615	0.384066471	0.662736842	0.222222222]
%Yt = simlssvm(model,x)
%[w1,b1,w2,b2]=initff(p',20,'logsig', 1,'logsig');
%disp_fqre=1;max_epoch=20000;err_goal=0.01;lr=0.05;
%tp=[disp_fqre  max_epoch  err_goal  lr];
%[w1,b1,w2,b2,te,tr]=trainbp(w1,b1,'logsig',w2,b2,'logsig',p',t',tp);
%a=simuff(x',w1,b1,'logsig',w2,b2,'logsig')