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Matlab

Matlab. Multilayer Perceptron. Multilayer: XOR. Input patterns. Multilayer : XOR. Target. Multilayer : XOR. New Network. Multilayer : XOR. View Network. Multilayer : XOR. Train. Multilayer : XOR. Performance. Multilayer : XOR. Regression. Multilayer : XOR. Test Data.

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Matlab

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  1. 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,letterH,letterI,letterJ,letterK,letterL,letterM,letterN,letterO,letterP,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];

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