This document provides an overview of artificial neural networks. It discusses biological neurons and artificial neurons, then describes models and algorithms for neural networks including perceptrons and multilayer perceptrons. It also covers topics like unsupervised learning, hybrid networks, radial basis functions, and provides examples of neural networks in SPSS and MATLAB.

1. ARTIFICIAL NEURAL NETWORK
1
Mahdi Akbarzadeh

2. 
Biological Neurons

Artificial Neurons
2

3. INTRODUCTION
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4. MODELS AND ALGORITHMS
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5. MODELS AND ALGORITHMS
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6. JARGON
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7. JARGON
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8. NETWORK DIAGRAM
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9. NETWORK DIAGRAM
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10. PERCEPTRONS
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11. PERCEPTRONS
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12. PERCEPTRONS
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13. PERCEPTRONS
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14. PERCEPTRONS
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15. PERCEPTRONS
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16. PERCEPTRONS
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17. MULTILAYER PERCEPTERON
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18. MULTILAYER PERCEPTERON
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19. MULTILAYER PERCEPTERON
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20. MULTILAYER PERCEPTERON
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21. MULTILAYER PERCEPTERON
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22. MULTILAYER PERCEPTERON
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23. MULTILAYER PERCEPTERON
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24. MULTILAYER PERCEPTERON
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25. MULTILAYER PERCEPTERON
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26. 26

27. 27

28. 28

29. 29

30. 30

31. 31

32. 32

33. Backpropagation
a
1
1  e  net g
g 
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34. hardlim(n) = 1
=0
if n >=0
otherwise
logsig(n) = 1 / (1 + exp(-n))
poslin(n) = n, if n >= 0
= 0, if n <= 0
34

35. purelin(n) = n
satlin(n) = 0, if n <= 0
= n, f 0 <= n <= 1
=1, if 1 <= n
satlins(n) = -1,
= n,
= 1,
if n <= -1
if -1 <= n <= 1
if 1 <= n
35

36. tansig(n) = 2/(1+exp(-2n))-1
tribas(n) = 1 - abs(n), if -1 <= n <= 1
= 0, otherwise
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radbas(n) = exp(-n^2)

37. 37

38. UNSUPERVISED LEARNING
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39. UNSUPERVISED LEARNING
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40. 40

41. 41

42. HYBRID NETWORK
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43. RADIAL BASIS FUNCTION (RBF)
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44. RADIAL BASIS FUNCTION (RBF)
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45. EXAMPLE
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46. 46

47. EXAMPLE: SPSS
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48. EXAMPLE: SPSS
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49. EXAMPLE: SPSS
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50. EXAMPLE: SPSS
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51. EXAMPLE: SPSS
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52. EXAMPLE: SPSS
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53. EXAMPLE: SPSS
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54. EXAMPLE: SPSS
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55. EXAMPLE: SPSS
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56. EXAMPLE: SPSS
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57. EXAMPLE: SPSS
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58. EXAMPLE: MATLAB
nprtool
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59. 59

60. 60

61. 61

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63. 63

64. 64

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