perception.m

多种人工神经网络：MATLAB源程序用于训练测试

%输入样本点及其相应的类别,其中有一个奇异点
a=[1,6.02876166666667,985.299,359.2246,285.8672,1002.45,950.1696,588.6987,5565.714,403.8405,986.1071,543.0317,3347.846,10526.29
2,6.55841166666667,1087.617,1089.121,1161.41,1039.121,1171.656,1032.836,3201.01,1252.184,876.5952,991.7463,614.1417,2166.554
3,9.136875,447.1318,77.42247,194.4386,126.2185,115.5564,66.29232,1698.92,107.0759,43.54045,158.174,595.0869,2241.519
4,9.69679,240.2039,150.4127,324.7353,328.3793,326.9558,451.9533,13749.83,368.2053,124.0888,279.9483,903.8962,7740.77
5,12.6517533333333,77.34785,46.73024,194.775,199.8224,207.8433,58.31628,1345.897,226.3404,189.3855,261.312,751.5944,1295.301
6,16.3797083333333,551.8002,159.1755,554.7005,560.9772,543.5907,36.22454,11267.9,185.4159,334.3091,31.4446,517.8536,7941.737
7,18.2904,901.9448,890.1299,932.7996,914.4736,896.8713,936.0389,3437.751,39.35514,533.7029,892.887,936.2882,3021.681
8,19.1128333333333,28.48561,41.25531,38.21983,35.38366,31.90228,38.2169,9010.457,617.5328,31.72406,39.57899,258.0887,8559.494
9,21.7294666666667,596.9189,21.27958,580.2268,607.4858,583.4184,585.2601,1211.529,18.7896,716.817,588.2169,707.5592,1039.775
10,22.7638833333333,4017.782,580.2765,3988.064,4040.178,4008.012,4006.004,3527.065,695.8135,5168.523,4106.439,3166.883,3419.812
11,24.9111666666667,127.4943,83.36885,92.7315,78.59083,104.4349,113.2928,2469.378,100.4127,68.73733,92.93684,517.353,1569.292
12,28.1498833333333,278.5096,30.52174,265.5422,293.8543,265.825,287.1093,2478.592,289.1985,209.7689,261.2818,313.0193,1308.21
13,30.8534166666667,213.4705,83.3774,206.1515,232.022,185.9995,230.7163,2379.286,216.4595,142.9059,197.8645,103.6648,2257.415
14,32.51025,710.6847,33.73828,598.1557,676.8867,596.8569,658.6823,542.6272,35.16835,25.28802,63.72168,161.4395,554.0054
15,34.3046833333333,319.8426,19.55383,284.3226,307.2647,281.0324,313.704,725.6993,16.77135,16.22373,28.84155,406.6724,574.84
16,36.0707833333333,295.6355,33.32183,30.5293,284.7257,29.6373,40.17825,399.9093,240.0002,219.1818,29.75243,103.4992,397.5261
17,39.3176833333333,70.91417,460.3932,77.28899,73.38973,101.9392,90.42711,3798.118,109.2916,171.9551,97.87682,1991.829,3919.963
18,39.9855833333333,13.47881,94.04516,16.97629,12.77151,13.69051,10.0131,635.324,58.83782,50.04967,19.16531,424.3428,447.987
19,44.18655,69.71117,90.75602,79.19471,71.55331,68.0253,70.11481,767.4713,29.36932,65.19684,60.66479,283.1253,689.6565];

P=a(:,2:13);
P=[-0.5  -0.5   0.3  -0.1  0.2   0.0   0.6  0.8  60;
    -0.5   0.5  -0.5   1.0  0.5  -0.9  0.8  -0.6 20];
T=[1 1 0 1 1 0 1 0 1];
%在坐标图上绘出样本点
plotpv(P,T);
%建立一个感知器网络
figure;
plotpv(P,T);
net=newp([-1 60;1 20],1);
handle=plotpc(net.iw{1},net.b{1});
%利用样本点训练网络并绘出得到的分类线
E=1;
while (sse(E)),
   [net,Y,E]=adapt(net,P,T);
   handle=plotpc(net.iw{1},net.b{1},handle);
end;
%局部放大分类线图
figure;
plotpv(P,T);
plotpc(net.iw{1},net.b{1});
axis([-2 2 -2 2]);
%选择10个点来测试网络
testpoints=[-0.5  0.3 -0.9  0.4 -0.1  0.2 -0.6  0.8  0.1 -0.4;
            -0.3 -0.8 -0.4 -0.7  0.4 -0.6  0.1 -0.5 -0.5  0.3];
a=sim(net,testpoints);
%在坐标图上绘出网络的分类结果及分类线
figure;
plotpv(testpoints,a);
plotpc(net.iw{1},net.b{1});