NEWRB, neurons = 0, SSE = 3.61745 NEWRB, neurons = 2, SSE = 2.75572 NEWRB, neurons = 3, SSE = 1.22387 NEWRB, neurons = 4, SSE = 0.537735 NEWRB, neurons = 5, SSE = 0.179913 NEWRB, neurons = 6, SSE = 0.0921845 NEWRB, neurons = 7, SSE = 0.0359042 NEWRB, neurons = 8, SSE = 3.15544e-029 MSE = 1.1687e-030 BP程序 clc; clear; close all; % 9组样本数据：
a=[0.2286,0.1292,0.0720,0.1592,0.1335,0.0733,0.1159,0.0940,0.0522,0.1345,0.0090,0.1260,0.3619,0.0690,0.1828
0.2090,0.0947,0.1393,0.1387,0.2558,0.0900,0.0771,0.0882,0.0393,0.1430,0.0126,0.1670,0.2450,0.0508,0.1328
0.0442,0.0880,0.1147,0.0563,0.3347,0.1150,0.1453,0.0429,0.1818,0.0378,0.0092,0.2251,0.1516,0.0858,0.0670
0.2603,0.1715,0.0702,0.2711,0.1491,0.1330,0.0968,0.1911,0.2545,0.0871,0.0060,0.1793,0.1002,0.0789,0.0909
0.3690,0.2222,0.0562,0.5157,0.1872,0.1614,0.1425,0.1506,0.1310,0.0500,0.0078,0.0348,0.0451,0.0707,0.0880
0.0359,0.1149,0.1230,0.5460,0.1977,0.1248,0.0624,0.0832,0.1640,0.1002,0.0059,0.1503,0.1837,0.1295,0.0700
0.1759,0.2347,0.1829,0.1811,0.2922,0.0655,0.0774,0.2273,0.2056,0.0925,0.0078,0.1852,0.3501,0.1680,0.2668
0.0724,0.1909,0.1340,0.2409,0.2842,0.0450,0.0824,0.1064,0.1909,0.1586
,0.0116,0.1698,0.3644,0.2718,0.2494
0.2634,0.2258,0.1165,0.1154,0.1074,0.0657,0.0610,0.2623,0.2588,0.1155,0.0050,0.0978,0.1511,0.2273,0.3220];
b=[1,0,0;1,0,0;1,0,0;0,1,0;0,1,0;0,1,0;0,0,1;0,0,1;0,0,1]; p=a'%RBF网络的15个输入向量 t=b'%RBF网络的3个输出向量 size(p) size(t)
net_1=newff(minmax(p),[10,3],{'tansig','purelin'},'traingdm') inputWeights=net_1.IW{1,1} inputbias=net_1.b{1} layerWeights=net_1.LW{2,1} layerbias=net_1.b{2} net_1.trainParam.show = 50; net_1.trainParam.lr = 0.05; net_1.trainParam.mc = 0.9; net_1.trainParam.epochs = 10000; net_1.trainParam.goal = 1e-3; [net_1,tr]=train(net_1,p,t); A = sim(net_1,p); E = t - A; MSE=mse(E) % 3组测试数据
ta=[0.2101,0.0950,0.1298,0.1359,0.2601,0.1001,0.0753,0.0890,0.0389,0.1451,0.0128,0.1590,0.2452,0.0512,0.1319
0.2593,0.1800,0.0711,0.2801,0.1501,0.1298,0.1001,0.1891,0.2531,0.0875,0.0058,0.1803,0.0992,0.0802,0.1002
0.2599,0.2235,0.1201,0.1171,0.1102,0.0683,0.0621,0.2597,0.2602,0.1167,0.0048,0.1002,0.1521,0.2281,0.3205];
tx=ta'
ty=sim(net_1,tx)%仿真输出 ty =
1.0257 0.0003 0.1151 -0.0417 0.9263 -0.0324 -0.0267 -0.0977 0.9535
MSE = 9.9944e-004