Hebbian学习规则 2神经元 简单实现

//hebbian_learning2.m

 1 %   Examples
 2 %   --------
 3 %   p1 = [1;1;-1;1];
 4 %   t1 = 0;
 5 %   p2 = [1;-1;1;1];
 6 %   t2 = 0;
 7 %   p3 = [-1;-1;-1;1]
 8 %   t3 = 1;
 9 %   w1 = hebbian_learning2(p1,t1,p2,t2,p3,t3)
10 %   t1 = 0;
11 %   t2 = 1;
12 %   t3 = 0;
13 %   w2 = hebbian_learning2(p1,t1,p2,t2,p3,t3)
14 function w = hebbian_learning2(p1,t1,p2,t2,p3,t3)
15 %   Author：Yao H. Wang
16 
17 %   hebbian_learning2 Summary of this function goes here
18 %   Detailed explanation goes here
19 %   w = TN。
20 %   其中T为t1,t2，t3即targets组成的矩阵。
21 %   N，当P的逆矩阵存在的时候为P的逆矩阵，否则为P的伪逆。
22 P = [p1';p2';p3']';
23 T = [t1,t2,t3];
24 [row,col] = size(P);
25 r = rank(P);
26 if (row == col) && (r==row)
27     N = inv(P);
28 else
29     N = (inv(P'*P))*P';
30 end
31 w = T*N;
32 
33 end

 

//hebbian_learning2test.m

 1 %   Examples
 2 %   --------
 3 %   p4 = [1;-1;1;-1];
 4 %   result = hebbian_learning2test(w1,w2,p4)
 5 function result = hebbian_learning2test(w1,w2,p)
 6 %   Author：Yao H. Wang
 7 
 8 %   hebbian_learning2test Summary of this function goes here
 9 %   Detailed explanation goes here
10 r1 = w1*p;
11 if r1<=0
12     r1 = 0;
13 else
14     r1 = 1;
15 end
16 r2 = w2*p;
17 if r2<=0
18     r2 = 0;
19 else
20     r2 = 1;
21 end
22 result = [r1,r2];
23 end

转载于:https://www.cnblogs.com/yaohwang/archive/2011/12/02/2367984.html