BP神经网络例二分类.docx
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BP神经网络例二分类
例二：
考虑如下花的分类数据
序号
萼片长度
萼片宽度
花瓣长度
花瓣宽度
类别
1
5.1
3.5
1.4
0.2
1
2
4.9
3
1.4
0.2
1
3
4.7
3.2
1.3
0.2
1
4
4.6
3.1
1.5
0.2
1
5
5
3.6
1.4
0.2
1
6
5.4
3.9
1.7
0.4
1
7
4.6
3.4
1.4
0.3
1
8
5
3.4
1.5
0.2
1
9
4.4
2.9
1.4
0.2
1
10
4.9
3.1
1.5
0.1
1
11
5.4
3.7
1.5
0.2
1
12
4.8
3.4
1.6
0.2
1
13
4.8
3
1.4
0.1
1
14
4.3
3
1.1
0.1
1
15
5.8
4
1.2
0.2
1
16
5.7
4.4
1.5
0.4
1
17
5.4
3.9
1.3
0.4
1
18
5.1
3.5
1.4
0.3
1
19
5.7
3.8
1.7
0.3
1
20
5.1
3.8
1.5
0.3
1
21
5.4
3.4
1.7
0.2
1
22
5.1
3.7
1.5
0.4
1
23
4.6
3.6
1
0.2
1
24
5.1
3.3
1.7
0.5
1
25
4.8
3.4
1.9
0.2
1
26
5
3
1.6
0.2
1
27
5
3.4
1.6
0.4
1
28
5.2
3.5
1.5
0.2
1
29
5.2
3.4
1.4
0.2
1
30
4.7
3.2
1.6
0.2
1
31
4.8
3.1
1.6
0.2
1
32
5.4
3.4
1.5
0.4
1
33
5.2
4.1
1.5
0.1
1
34
5.5
4.2
1.4
0.2
1
35
4.9
3.1
1.5
0.1
1
36
5
3.2
1.2
0.2
1
37
5.5
3.5
1.3
0.2
1
38
4.9
3.1
1.5
0.1
1
39
4.4
3
1.3
0.2
1
40
5.1
3.4
1.5
0.2
1
41
5
3.5
1.3
0.3
1
42
4.5
2.3
1.3
0.3
1
43
4.4
3.2
1.3
0.2
1
44
5
3.5
1.6
0.6
1
45
5.1
3.8
1.9
0.4
1
46
4.8
3
1.4
0.3
1
47
5.1
3.8
1.6
0.2
1
48
4.6
3.2
1.4
0.2
1
49
5.3
3.7
1.5
0.2
1
50
5
3.3
1.4
0.2
1
51
7
3.2
4.7
1.4
2
52
6.4
3.2
4.5
1.5
2
53
6.9
3.1
4.9
1.5
2
54
5.5
2.3
4
1.3
2
55
6.5
2.8
4.6
1.5
2
56
5.7
2.8
4.5
1.3
2
57
6.3
3.3
4.7
1.6
2
58
4.9
2.4
3.3
1
2
59
6.6
2.9
4.6
1.3
2
60
5.2
2.7
3.9
1.4
2
61
5
2
3.5
1
2
62
5.9
3
4.2
1.5
2
63
6
2.2
4
1
2
64
6.1
2.9
4.7
1.4
2
65
5.6
2.9
3.6
1.3
2
66
6.7
3.1
4.4
1.4
2
67
5.6
3
4.5
1.5
2
68
5.8
2.7
4.1
1
2
69
6.2
2.2
4.5
1.5
2
70
5.6
2.5
3.9
1.1
2
71
5.9
3.2
4.8
1.8
2
72
6.1
2.8
4
1.3
2
73
6.3
2.5
4.9
1.5
2
74
6.1
2.8
4.7
1.2
2
75
6.4
2.9
4.3
1.3
2
76
6.6
3
4.4
1.4
2
77
6.8
2.8
4.8
1.4
2
78
6.7
3
5
1.7
2
79
6
2.9
4.5
1.5
2
80
5.7
2.6
3.5
1
2
81
5.5
2.4
3.8
1.1
2
82
5.5
2.4
3.7
1
2
83
5.8
2.7
3.9
1.2
2
84
6
2.7
5.1
1.6
2
85
5.4
3
4.5
1.5
2
86
6
3.4
4.5
1.6
2
87
6.7
3.1
4.7
1.5
2
88
6.3
2.3
4.4
1.3
2
89
5.6
3
4.1
1.3
2
90
5.5
2.5
4
1.3
2
91
5.5
2.6
4.4
1.2
2
92
6.1
3
4.6
1.4
2
93
5.8
2.6
4
1.2
2
94
5
2.3
3.3
1
2
95
5.6
2.7
4.2
1.3
2
96
5.7
3
4.2
1.2
2
97
5.7
2.9
4.2
1.3
2
98
6.2
2.9
4.3
1.3
2
99
5.1
2.5
3
1.1
2
100
5.7
2.8
4.1
1.3
2
101
6.3
3.3
6
2.5
3
102
5.8
2.7
5.1
1.9
3
103
7.1
3
5.9
2.1
3
104
6.3
2.9
5.6
1.8
3
105
6.5
3
5.8
2.2
3
106
7.6
3
6.6
2.1
3
107
4.9
2.5
4.5
1.7
3
108
7.3
2.9
6.3
1.8
3
109
6.7
2.5
5.8
1.8
3
110
7.2
3.6
6.1
2.5
3
111
6.5
3.2
5.1
2
3
112
6.4
2.7
5.3
1.9
3
113
6.8
3
5.5
2.1
3
114
5.7
2.5
5
2
3
115
5.8
2.8
5.1
2.4
3
116
6.4
3.2
5.3
2.3
3
117
6.5
3
5.5
1.8
3
118
7.7
3.8
6.7
2.2
3
119
7.7
2.6
6.9
2.3
3
120
6
2.2
5
1.5
3
121
6.9
3.2
5.7
2.3
3
122
5.6
2.8
4.9
2
3
123
7.7
2.8
6.7
2
3
124
6.3
2.7
4.9
1.8
3
125
6.7
3.3
5.7
2.1
3
126
7.2
3.2
6
1.8
3
127
6.2
2.8
4.8
1.8
3
128
6.1
3
4.9
1.8
3
129
6.4
2.8
5.6
2.1
3
130
7.2
3
5.8
1.6
3
131
7.4
2.8
6.1
1.9
3
132
7.9
3.8
6.4
2
3
133
6.4
2.8
5.6
2.2
3
134
6.3
2.8
5.1
1.5
3
135
6.1
2.6
5.6
1.4
3
136
7.7
3
6.1
2.3
3
137
6.3
3.4
5.6
2.4
3
138
6.4
3.1
5.5
1.8
3
139
6
3
4.8
1.8
3
140
6.9
3.1
5.4
2.1
3
141
6.7
3.1
5.6
2.4
3
142
6.9
3.1
5.1
2.3
3
143
5.8
2.7
5.1
1.9
3
144
6.8
3.2
5.9
2.3
3
145
6.7
3.3
5.7
2.5
3
146
6.7
3
5.2
2.3
3
147
6.3
2.5
5
1.9
3
148
6.5
3
5.2
2
3
149
6.2
3.4
5.4
2.3
3
150
5.9
3
5.1
1.8
3
这是一个三类问题，为了验证算法的性能，用每类的前25个数据（共75）作为训练样本，用BP神经网络进行建模，并对剩下的样本用该网络进行判别。
训练样本如下：
5.1
3.5
1.4
0.2
1
6.7
3.1
4.4
1.4
2
4.9
3
1.4
0.2
1
5.6
3
4.5
1.5
2
4.7
3.2
1.3
0.2
1
5.8
2.7
4.1
1
2
4.6
3.1
1.5
0.2
1
6.2
2.2
4.5
1.5
2
5
3.6
1.4
0.2
1
5.6
2.5
3.9
1.1
2
5.4
3.9
1.7
0.4
1
5.9
3.2
4.8
1.8
2
4.6
3.4
1.4
0.3
1
6.1
2.8
4
1.3
2
5
3.4
1.5
0.2
1
6.3
2.5
4.9
1.5
2
4.4
2.9
1.4
0.2
1
6.1
2.8
4.7
1.2
2
4.9
3.1
1.5
0.1
1
6.4
2.9
4.3
1.3
2
5.4
3.7
1.5
0.2
1
6.3
3.3
6
2.5
3
4.8
3.4
1.6
0.2
1
5.8
2.7
5.1
1.9
3
4.8
3
1.4
0.1
1
7.1
3
5.9
2.1
3
4.3
3
1.1
0.1
1
6.3
2.9
5.6
1.8
3
5.8
4
1.2
0.2
1
6.5
3
5.8
2.2
3
5.7
4.4
1.5
0.4
1
7.6
3
6.6
2.1
3
5.4
3.9
1.3
0.4
1
4.9
2.5
4.5
1.7
3
5.1
3.5
1.4
0.3
1
7.3
2.9
6.3
1.8
3
5.7
3.8
1.7
0.3
1
6.7
2.5
5.8
1.8
3
5.1
3.8
1.5
0.3
1
7.2
3.6
6.1
2.5
3
5.4
3.4
1.7
0.2
1
6.5
3.2
5.1
2
3
5.1
3.7
1.5
0.4
1
6.4
2.7
5.3
1.9
3
4.6
3.6
1
0.2
1
6.8
3
5.5
2.1
3
5.1
3.3
1.7
0.5
1
5.7
2.5
5
2
3
4.8
3.4
1.9
0.2
1
5.8
2.8
5.1
2.4
3
7
3.2
4.7
1.4
2
6.4
3.2
5.3
2.3
3
6.4
3.2
4.5
1.5
2
6.5
3
5.5
1.8
3
6.9
3.1
4.9
1.5
2
7.7
3.8
6.7
2.2
3
5.5
2.3
4
1.3
2
7.7
2.6
6.9
2.3
3
6.5
2.8
4.6
1.5
2
6
2.2
5
1.5
3
5.7
2.8
4.5
1.3
2
6.9
3.2
5.7
2.3
3
6.3
3.3
4.7
1.6
2
5.6
2.8
4.9
2
3
4.9
2.4
3.3
1
2
7.7
2.8
6.7
2
3
6.6
2.9
4.6
1.3
2
6.3
2.7
4.9
1.8
3
5.2
2.7
3.9
1.4
2
6.7
3.3
5.7
2.1
3
5
2
3.5
1
2
5.9
3
4.2
1.5
2
6
2.2
4
1
2
6.1
2.9
4.7
1.4
2
5.6
2.9
3.6
1.3
2
检验样本
5
3
1.6
0.2
1
5.5
2.6
4.4
1.2
2
5
3.4
1.6
0.4
1
6.1
3
4.6
1.4
2
5.2
3.5
1.5
0.2
1
5.8
2.6
4
1.2
2
5.2
3.4
1.4
0.2
1
5
2.3
3.3
1
2
4.7
3.2
1.6
0.2
1
5.6
2.7
4.2
1.3
2
4.8
3.1
1.6
0.2
1
5.7
3
4.2
1.2
2
5.4
3.4
1.5
0.4
1
5.7
2.9
4.2
1.3
2
5.2
4.1
1.5
0.1
1
6.2
2.9
4.3
1.3
2
5.5
4.2
1.4
0.2
1
5.1
2.5
3
1.1
2
4.9
3.1
1.5
0.1
1
5.7
2.8
4.1
1.3
2
5
3.2
1.2
0.2
1
7.2
3.2
6
1.8
3
5.5
3.5
1.3
0.2
1
6.2
2.8
4.8
1.8
3
4.9
3.1
1.5
0.1
1
6.1
3
4.9
1.8
3
4.4
3
1.3
0.2
1
6.4
2.8
5.6
2.1
3
5.1
3.4
1.5
0.2
1
7.2
3
5.8
1.6
3
5
3.5
1.3
0.3
1
7.4
2.8
6.1
1.9
3
4.5
2.3
1.3
0.3
1
7.9
3.8
6.4
2
3
4.4
3.2
1.3
0.2
1
6.4
2.8
5.6
2.2
3
5
3.5
1.6
0.6
1
6.3
2.8
5.1
1.5
3
5.1
3.8
1.9
0.4
1
6.1
2.6
5.6
1.4
3
4.8
3
1.4
0.3
1
7.7
3
6.1
2.3
3
5.1
3.8
1.6
0.2
1
6.3
3.4
5.6
2.4
3
4.6
3.2
1.4
0.2
1
6.4
3.1
5.5
1.8
3
5.3
3.7
1.5
0.2
1
6
3
4.8
1.8
3
5
3.3
1.4
0.2
1
6.9
3.1
5.4
2.1
3
6.6
3
4.4
1.4
2
6.7
3.1
5.6
2.4
3
6.8
2.8
4.8
1.4
2
6.9
3.1
5.1
2.3
3
6.7
3
5
1.7
2
5.8
2.7
5.1
1.9
3
6
2.9
4.5
1.5
2
6.8
3.2
5.9
2.3
3
5.7
2.6
3.5
1
2
6.7
3.3
5.7
2.5
3
5.5
2.4
3.8
1.1
2
6.7
3
5.2
2.3
3
5.5
2.4
3.7
1
2
6.3
2.5
5
1.9
3
5.8
2.7
3.9
1.2
2
6.5
3
5.2
2
3
6
2.7
5.1
1.6
2
6.2
3.4
5.4
2.3
3
5.4
3
4.5
1.5
2
5.9
3
5.1
1.8
3
6
3.4
4.5
1.6
2
6.7
3.1
4.7
1.5
2
6.3
2.3
4.4
1.3
2
5.6
3
4.1
1.3
2
5.5
2.5
4
1.3
2
用BP神经网络对数据进行分类源程序如下：
p=[5.1,3.5,1.4,0.2;
4.9,3.0,1.4,0.2;
4.7,3.2,1.3,0.2;
4.6,3.1,1.5,0.2;
5.0,3.6,1.4,0.2;
5.4,3.9,1.7,0.4;
4.6,3.4,1.4,0.3;
5.0,3.4,1.5,0.2;
4.4,2.9,1.4,0.2;
4.9,3.1,1.5,0.1;
5.4,3.7,1.5,0.2;
4.8,3.4,1.6,0.2;
4.8,3.0,1.4,0.1;
4.3,3.0,1.1,0.1;
5.8,4.0,1.2,0.2;
5.7,4.4,1.5,0.4;
5.4,3.9,1.3,0.4;
5.1,3.5,1.4,0.3;
5.7,3.8,1.7,0.3;
5.1,3.8,1.5,0.3;
5.4,3.4,1.7,0.2;
5.1,3.7,1.5,0.4;
4.6,3.6,1.0,0.2;
5.1,3.3,1.7,0.5;
4.8,3.4,1.9,0.2;
7.0,3.2,4.7,1.4;
6.4,3.2,4.5,1.5;
6.9,3.1,4.9,1.5;
5.5,2.3,4.0,1.3;
6.5,2.8,4.6,1.5;
5.7,2.8,4.5,1.3;
6.3,3.3,4.7,1.6;
4.9,2.4,3.3,1.0;
6.6,2.9,4.6,1.3;
5.2,2.7,3.9,1.4;
5.0,2.0,3.5,1.0;
5.9,3.0,4.2,1.5;
6.0,2.2,4.0,1.0;
6.1,2.9,4.7,1.4;
5.6,2.9,3.6,1.3;
6.7,3.1,4.4,1.4;
5.6,3.0,4.5,1.5;
5.8,2.7,4.1,1.0;
6.2,2.2,4.5,1.5;
5.6,2.5,3.9,1.1;
5.9,3.2,4.8,1.8;
6.1,2.8,4.0,1.3;
6.3,2.5,4.9,1.5;
6.1,2.8,4.7,1.2;
6.4,2.9,4.3,1.3;
6.3,3.3,6.0,2.5;
5.8,2.7,5.1,1.9;
7.1,3.0,5.9,2.1;
6.3,2.9,5.6,1.8;
6.5,3.0,5.8,2.2;
7.6,3.0,6.6,2.1;
4.9,2.5,4.5,1.7;
7.3,2.9,6.3,1.8;
6.7,2.5,5.8,1.8;
7.2,3.6,6.1,2.5;
6.5,3.2,5.1,2.0;
6.4,2.7,5.3,1.9;
6.8,3.0,5.5,2.1;
5.7,2.5,5.0,2.0;
5.8,2.8,5.1,2.4;
6.4,3.2,5.3,2.3;
6.5,3.0,5.5,1.8;
7.7,3.8,6.7,2.2;
7.7,2.6,6.9,2.3;
6.0,2.2,5.0,1.5;
6.9,3.2,5.7,2.3;
5.6,2.8,4.9,2.0;
7.7,2.8,6.7,2.0;
6.3,2.7,4.9,1.8;
6.7,3.3,5.7,2.1]';
fori=1:
4
P（i,:
）=（p（i,:
）-min（p（i,:
）））/（max（p（i,:
））-min（p（i,:
）））;
end
T=[100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
100;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
010;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001;
001]';
threshold=[01;01;01;01];
net=newff（threshold,[9,3],{'tansig','logsig'},'trainlm'）;
net=train（net,P,T）;
y_test=sim（net,P）'
p_test=[5.0,3.0,1.6,0.2;
5.0,3.4,1.6,0.4;
5.2,3.5,1.5,0.2;
5.2,3.4,1.4,0.2;
4.7,3.2,1.6,0.2;
4.8,3.1,1.6,0.2;
5.4,3.4,1.5,0.4;
5.2,4.1,1.5,0.1;
5.5,4.2,1.4,0.2;
4.9,3.1,1.5,0.1;
5.0,3.2,1.2,0.2;
5.5,3.5,1.3,0.2;
4.9,3.1,1.5,0.1;
4.4,3.0,1.3,0.2;
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