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Naturally Intelligent Systems

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For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. Now there is a new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks, clearly explaining the underlying concepts of key neural network designs, how they are trained, and why they work. Throughout, the authors present actual applications that illustrate neural networks' utility in the new world.

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Editorial Reviews

Explains the underlying concepts of neural computers, how they are trained, and why they work. Compares their behavior to the intelligence found in humans. Assessible to the lay reader. No bibliography. Annotation c. Book News, Inc., Portland, OR
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Product Details

  • ISBN-13: 9780262531139
  • Publisher: MIT Press
  • Publication date: 9/8/1992
  • Series: Bradford Books Series
  • Edition description: New Edition
  • Pages: 316
  • Product dimensions: 5.90 (w) x 8.90 (h) x 0.80 (d)

Meet the Author

Maureen Caudill is President of Adaptics, a neural network consulting company in San Diego, and author of the popular "Neural Network Primer" articles that appear regularly in AI Expert.

Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria,Virginia. He is a specialist in neural network application development.

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Table of Contents

Part I
Natural Intelligence
Neural Networks in a Nutshell
Characteristics of Neural Networks
Comparing a Neural Network to a Digital Computer
Building Blocks of Neural Networks
The Anatomy of a Neurode
The Network in Action
Implementation: The Artificial Retina
Neural Networks and Other System
Neural Networks and Artificial Intelligence
Neural Networks and Parallel Computers
Do Neurodes Have Internal Memory?
Relationship to Analog Computers
Part II
Associative Memories
Features of Associative Memories
Classes of Neural Network Associative Memories
Using an Associative Memory
Neural Network Models
Crossbar Associative Memories
Energy Surface Representation
Matrix Representation of Crossbar Networks
Feedback Competition Representation
An Illustrative Example
The Problems with Crossbar Networks
Then Why Study the Crossbar?
Adaptive Filter Associative Memories
Introducing the Adaline
Geometry of the Delta Rule
Choosing the Learning Constant
What Can the Adaline Do?
Limitations of the Adaline
Training the Madaline
Higher-Order Networks
The Polynomial Adaline
Application: The Vectorcardiograph
Competitive Filter Associative Memories
A Self-Organizing Architecture
Lateral Inhibition
The Network in Operation
The Geometry of the Network
The Crust Thickens
Training Techniques
The Topology-Preserving Map
Application: The Voice Typewriter
Part III
Types of Learning
Learning and Memory in Neural Networks
Training a Neural Network
Hebbian Learning
Neohebbian Learning
Differential Hebbian Learning
Classical or Pavlovian Conditioning
The Instar and Outstar
Outstar Learning
Outstar Inconsistencies
Drive-Reinforcement Theory
Learning Sequences of Patterns
The Music Box Associative Memory
Single-Neurode Systems
The Outstar Avalanche
Recognizing Sequences of Patterns
Autonomous Learning
Characteristics of Autonomous Learning Systems
Recall by Association
Seek and Ye Shall Find
Dealing with Reality
The Importance of Being Significant
On with the New, On with the Old
Categorically Speaking
The Memory Shell Game
Elementary, My Dear Watson
An Ocean of Experience
Autonomous Learning Redux
A Classic System: The Perceptron
Part IV
Hierarchical Systems
Linear Separability
Kolmogorov's Theorem
What Kolmogorov Didn't Say
Application: The Neocognitron
Backpropagation Networks
Features of Backpropagation Systems
Building a Backpropagation System
The Backpropagation Process
Limitations of Backpropagation Networks
Variations of the Generalized Delta Rule
Scaling Problems
Biological Arguments against Backpropagation
Applications of Backpropagation Systems
Application: NETtalk
Hybrid Networks
The Counterpropagation Network
Training Techniques and Problems
The Size of the Middle Layer
Using the Counterpropagation Network
Adaptive Resonance Networks
The Principle of Adaptive Resonance
Operation of the ART 1 Network
The Reset Subsystem
The Gain Control Subsystem and the 2/3 Rule
Troubles with ART 1
Art 2
Grandmother Nodes and ART
The Limitations of ART Networks in General
Part V
Neural Network Implementations
Software Simulations
Networks in Hardware
Optical Neural Networks
Neural Network
An Expert Mortgage Insurance Underwriter
A Process Controller That the Freeway
A Robotic Arm
Sonar Signal Processing
A Look Ahead
Implementations Development
Weird Science
Neural Network Theory
A Final Word
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  • Anonymous

    Posted February 15, 2001

    More diagrams would help

    It is often said that a picture is worth a thousand words. Well, this book chose the thousand-word route in most cases. Neural nets are ideal candidates for illustrations. So, why they decided to use endless descriptions is beyond me. It is like giving directions over the phone when a map would get to the point much faster. It also needed to explain more conceptually how neural nets actually work, not just how they are arranged. Examples where the net matches one-to-one with an actual image or pattern are easy to follow, but how they recognize different variations of patterns (variety) I never got a good feel for from this book. However, the description of an Adeline node was pretty good.

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