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Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2010

Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2010. Lecture 1 September 13, 2010. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall10/ann/. http://www.youtube.com/watch?v=gy5g33S0Gzo. March 17, 2010. Plan.

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Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2010

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  1. Artificial Neural NetworksECE.09.454/ECE.09.560Fall 2010 Lecture 1September 13, 2010 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall10/ann/

  2. http://www.youtube.com/watch?v=gy5g33S0Gzo March 17, 2010

  3. Plan • What is artificial intelligence? • Course introduction • Historical development – the neuron model • The artificial neural network paradigm • What is knowledge? What is learning? • The Perceptron • Widrow-Hoff Learning Rule • The “Future”….?

  4. Systems that think rationally • Logic • Systems that think like humans • Cognitive modeling • Systems that act rationally • Decision theoretic agents • Systems that act like humans • Natural language processing • Knowledge representation • Machine learning Artificial Intelligence

  5. Course Introduction • Why should we take this course? • PR, Applications • What are we studying in this course? • Course objectives/deliverables • How are we conducting this course? • Course logistics • http://engineering.rowan.edu/shreek/fall10/ann/

  6. Course Objectives • At the conclusion of this course the student will be able to: • Identify and describe engineering paradigms for knowledge and learning • Identify, describe and design artificial neural network architectures for simple cognitive tasks

  7. Biological Origins

  8. Biological Origins

  9. History/People

  10. Indicate Desired Outputs Determine Synaptic Weights Predicted Outputs Neural Network Paradigm Stage 1: Network Training Artificial Neural Network Present Examples “knowledge” Stage 2: Network Testing Artificial Neural Network New Data

  11. ANN Model x Input Vector y Output Vector Artificial Neural Network f Complex Nonlinear Function f(x) = y “knowledge”

  12. Single output ANN x y 1-out-of-c selector Coder Associator ANN ANN x x yc yc y2 y2 y1 y1 ANN x y Popular I/O Mappings

  13. The Perceptron Activation/ squashing function wk1 Bias, bk x1 wk2 x2 S S j(.) Output, yk Inputs uk Induced field, vk wkm xm Synaptic weights

  14. Activation Functions Threshold Sigmoid

  15. “Learning” Mathematical Model of the Learning Process Intitialize: Iteration (0) ANN [w]0 x y(0) [w] x y Iteration (1) [w]1 x y(1) desired o/p Iteration (n) [w]n x y(n) = d

  16. “Learning” Mathematical Model of the Learning Process Intitialize: Iteration (0) ANN [w]0 x y(0) [w] x y Iteration (1) [w]1 x y(1) desired o/p Iteration (n) [w]n x y(n) = d

  17. Error-Correction Learning Desired Output, dk (n) wk1(n) Activation/ squashing function x1 (n) Bias, bk wk2(n) x2 + Output, yk (n) S S j(.) Inputs Synaptic weights - Induced field, vk(n) wkm(n) Error Signal ek (n) xm

  18. Pattern Association Pattern Recognition Function Approximation Filtering x2 x2 2 2 DB 1 1 DB x1 x1 Learning Tasks Classification

  19. Perceptron Training Widrow-Hoff Rule (LMS Algorithm) w(0) = 0 n = 0 y(n) = sgn [wT(n) x(n)] w(n+1) = w(n) + h[d(n) – y(n)]x(n) n = n+1 Matlab Demo

  20. The Age of Spiritual MachinesWhen Computers Exceed Human Intelligenceby Ray Kurzweil | Penguin paperback | 0-14-028202-5 |

  21. Summary

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