1. Multilayer Perceptron
Matlab
Multilayer Perceptron

2. Multilayer: XOR
Input patterns

3. Multilayer : XOR
Target

4. Multilayer : XOR
New Network

5. Multilayer : XOR
View Network

6. Multilayer : XOR
Train

7. Multilayer : XOR
Performance

8. Multilayer : XOR
Regression

9. Multilayer : XOR
Test Data

10. Multilayer : XOR
Simulate

11. Multilayer : XOR
Simulate

12. Classification: Character recognition
APPCR1
PRPROB

13. Classification: Character recognition
Input patterns

14. Classification: Character recognition
Input patterns
alphabet = [letterA,letterB,letterC,letterD,letterE,letterF,letterG,lette rH,letterI,letterJ,letterK,letterL,letterM,letterN,letterO,let terP,letterQ,letterR,letterS,letterT,letterU,letterV,letterW, letterX,letterY,letterZ];

15. Classification: Character recognition
Input patterns: suffer from noise
alpha_noise= alphabet + randn(35,26)*0.5;

16. Classification: Character recognition
Input patterns: All
p=[alphabet alpha_noise];

17. Classification: Character recognition
Target
T= [eye(26) eye(26)];

18. Classification: Character recognition
New Network

19. Classification: Character recognition
View Network

20. Classification: Character recognition
Train

21. Classification: Character recognition
Performance

22. Classification: Character recognition
Regression

23. Classification: Character recognition
Test Data
test_p = alphabet + randn(35,26)*0.25;

24. Classification: Character recognition
Simulate
export

25. Classification: Character recognition
Simulate
multilayer_char_test_out_2= compet(multilayer_char_test_out);

26. Classification: Character recognition
Simulate
error= sum(sum(abs(multilayer_char_test_out_2-eye(26))))/2;
25!!!!!!!!!!!

27. Function Approimation: Sin
Input patterns:
p=[-1:0.05:1];
p=2*pi*p;

28. Function Approimation: Sin
Target
t=sin(p)+0.1*randn(size(p));

29. Function Approimation: Sin
plot(p, t, 'DisplayName', 'p', 'XDataSource', 'p', 'YDataSource', 't'); figure(gcf)

30. Function Approimation: Sin
New Network

31. Function Approimation: Sin
View Network

32. Function Approximation: Sin
Train

33. Function Approximation: Sin
Performance

34. Function Approximation: Sin
Regression

35. Function Approximation: Sin
Simulate
testp=[pi/6, pi/4, pi/3, pi/2];