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Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2008. Lecture 1 September 8, 2008. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall08/ann/. Plan. What is artificial intelligence? Course introduction

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artificial neural networks ece 09 454 ece 09 560 fall 2008

Artificial Neural NetworksECE.09.454/ECE.09.560Fall 2008

Lecture 1September 8, 2008

Shreekanth Mandayam

ECE Department

Rowan University

http://engineering.rowan.edu/~shreek/fall08/ann/

slide2
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”….?
artificial intelligence

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
course introduction
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/fall08/ann/
course objectives
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
neural network paradigm

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

ann model
ANN Model

x

Input

Vector

y

Output

Vector

Artificial

Neural

Network

f

Complex

Nonlinear

Function

f(x) = y

“knowledge”

popular i o mappings

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
the perceptron
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

learning
“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

learning14
“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

error correction learning
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

learning tasks
Pattern Association

Pattern Recognition

Function Approximation

Filtering

x2

x2

2

2

DB

1

1

DB

x1

x1

Learning Tasks

Classification

perceptron training widrow hoff rule lms algorithm
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

slide18

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