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TWEET Untitled a guest Feb 12th, 2019 60 Never
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1. clear;
2. clc;
3.
4. epochs=100;
5. learning_rate=0.1;
6. hidden_nodes=80;
7. place=linspace(0,2*pi,hidden_nodes);
8. var=0.07;
9.
10. %training and test sets
11. x=(0:0.01:2*pi);
12. test_set=(0.05:0.01:6.33);
13.
14. %gaussian noise
15. variance=0.6;
16. noise=sqrt(variance).*randn(1,length(x));
17.
18. %Target sets
19. t1=sin(2*x)+noise;
20. t2=square(2*x)+noise;
21.
22. weight_matrix=rand(hidden_nodes,1);
23. first_weight=weight_matrix;
24. for i=1:epochs
25.     for j=1:length(x)
26.
27.         %plot the output of hidden nodes
28.         for k=1:length(place)
29.         y(k,:)=rbf(test_set(k),place(k),var);
30.         %plot(y(k,:))
31.         phi=y';
32.         end
33.
34.         out=phi*weight_matrix;
35.         error=t1(j)-out;
36.         delta=learning_rate * error * y;
37.         %delta
38.         weight_matrix=weight_matrix+delta;
39.     end
40.         permute = randperm(length(x));
41.         % Permute data
42.         x = x(:, permute);
43. end
44.
45.     %results
46.     for i=1:length(place)
47.         a(k,:)=rbf(x,place(k),var);
48.         %plot(y(k,:))
49.         phi=a';
50.     end
51.
52.         out1=phi*first_weight;
53.         out2=phi*weight_matrix;
54.         plot(t1,'g')
55.         hold on
56.         plot(out1','r')
57.         hold on
58.         plot(out2,'b')
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