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Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002

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Systems that act rationally. Decision theoretic agents ... yc. ANN. x. Associator. y. S. Mandayam/ ANN/ECE Dept./Rowan University. The Perceptron. S ... – PowerPoint PPT presentation

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Title: Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002


1
Artificial Neural Networks0909.560.01/0909.454.01
Spring 2002
Lecture 1January 24, 2002
  • Shreekanth Mandayam
  • Robi Polikar
  • ECE Department
  • Rowan University
  • http//engineering.rowan.edu/shreek/spring02/ann/

2
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
  • The Future.?

3
Artificial Intelligence
4
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/spring02/ann/

5
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

6
Biological Origins
7
Biological Origins
8
History/People
1940s Turing General problem solver, Turing test
1940s Shannon Information theory
1943 McCulloch and Pitts Math of neural processes
1949 Hebb Learning model
1959 Rosenblatt The Perceptron
1960 Widrow LMS training algorithm
1969 Minsky and Papert Perceptron deficiency
1985 Rumelhart Feedforward MLP, backprop
1988 Broomhead and Lowe Radial basis function neural nets
1990s VLSI implementations
9
Neural Network Paradigm
Stage 1 Network Training
Artificial Neural Network
Present Examples
knowledge
Stage 2 Network Testing
Artificial Neural Network
New Data
10
ANN Model
x Input Vector
y Output Vector
Artificial Neural Network
f Complex Nonlinear Function
f(x) y
knowledge
11
Popular I/O Mappings
12
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
13
Learning
Mathematical Model of the Learning Process
Intitialize Iteration (0)
ANN
w0
x
y(0)
w
x
y
Iteration (1)
w1
x
y(1)
desired o/p
Iteration (n)
wn
x
y(n) d
14
The Age of Spiritual MachinesWhen Computers
Exceed Human Intelligenceby Ray Kurzweil
Penguin paperback 0-14-028202-5
15
Summary
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