Handbook of Neural Computing Applications - 1st Edition - ISBN: 9780125460903, 9781483264844

Handbook of Neural Computing Applications

1st Edition

Authors: Alianna J. Maren Craig T. Harston Robert M. Pap
eBook ISBN: 9781483264844
Imprint: Academic Press
Published Date: 1st January 1990
Page Count: 470
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Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.

Table of Contents



1 Introduction to Neural Networks

1.0 Overview

1.1 Practical Applications

1.2 The Advantages of Neural Networks

1.3 A Definition of Neural Networks

1.4 Summary


2 History and Development of Neural Networks

2.0 Overview

2.1 Early Foundations

2.2 Promising and Emerging Technology

2.3 Disenchantment

2.4 Innovation

2.5 Re-Emergence

2.6 Current Status

2.7 Summary


3 The Neurological Basis for Neural Computations

3.0 Neuroscience As A Model

3.1 The Single Neuron

3.2 Early Research

3.3 Structural Organization of Biological Neural Systems

3.4 Structurally Linked Dynamics of Biological Neural Systems

3.5 Emergent Properties Arise from the Dynamics of Biological Neural Systems

3.6 Learning in Biological Neural Systems

3.7 Functional Results of Neural Architecture

3.8 Computer Simulations Based on the Brain


4 Neural Network Structures: Form Follows Function

4.0 Overview

4.1 Levels of Structural Description

4.2 Neural Micro-Structures

4.3 Neural Meso-Structures

4.4 The Macro-Structure

4.5 Summary

5 Dynamics of Neural Network Operations

5.0 Overview

5.1 Typical Network Dynamics

5.2 Energy Surfaces and Stability Criterion

5.3 Network Structures and Dynamics


6 Learning Background for Neural Networks

6.0 Overview

6.1 Intelligence: An Operational Definition

6.2 Learning and Conditioning

6.3 Learned Performance

6.4 Motivation

6.5 Summary


7 Multilayer Feedforward Neural Networks I: Delta Rule Learning

7.0 Overview

7.1 Introduction

7.2 The Perceptron Network

7.3 Adaline and Madaline Neural Networks

7.4 The Back-Propagation Network


8 Multilayer Feedforward Neural Networks II: Optimizing Learning Methods

8.0 Overview

8.1 The Boltzmann Machine

8.2 The Cauchy Machine: A Refinement of the Boltzmann Machine

8.3 Summary


9 Laterally-Connected, Autoassociative Networks

9.0 Overview

9.1 Introduction to Association Networks

9.2 Auto Associative Networks

9.3 The Hopfield/Tank Network

9.4 The Brain-State-In-A-Box Network

9.5 Kanerva's Sparse Distributed Memory Network

9.6 Summary


10 Vector-Matching Networks

10.0 Overview

10.1 Introduction

10.2 The Kohonen Learning Vector Quantization Network

10.3 The Self-Organizing Topology-Preserving Map

10.4 Summary


11 Feedforward/Feedback (Resonating) Heteroassociative Networks

11.0 Chapter Overview

11.1 Introduction

11.2 The Carpenter/Grossberg Adaptive Resonance Theory Network

11.3 Bidirectional Associative Memories and Related Networks

11.4 Summary


12 Multilayer Cooperative/Competitive Networks

12.0 Overview

12.1 Introduction

12.2 Competitive Learning Networks

12.3 Masking Fields

12.4 The Boundary Contour System

12.5 Hierarchical Scene Structures

12.6 The Neocognitron

12.7 Summary


13 Hybrid and Complex Networks

13.0 Overview

13.1 Introduction

13.2 Hybrid Networks: The Hamming Network and the Counter-Propagation Network

13.3 Neural Networks Operating in Parallel

13.4 Hierarchies of Similar Networks

13.5 Systems of Different Types of Neural Networks

13.6 Systems of Networks are Useful for Adaptive Control

13.7 Summary


14 Choosing A Network: Matching the Architecture to the Application

14.0 Chapter Overvie

14.1 When to use A Neural Network

14.2 What Type of Network?

14.3 Debugging, Testing, and Verifying Neural Network Codes

14.4 Implementing Neural Networks


15 Configuring and Optimizing the Back-Propagation Network

15.0 Overview

15.1 Issues in Optimizing and Generalizing Feedforward Networks

15.2 Micro-Structural Considerations

15.3 Meso-Structural Considerations

15.4 Optimizing Network Dynamics

15.5 Learning Rule Modifications

15.6 Modifications to Network Training Schedules and Datasets


16 Electronic Hardware Implementations

16.0 Overview

16.1 Analog Implementations

16.2 Digital Neural Network Chips

16.3 Hybrid Neural Network Chips

16.4 Method for Comparing Neural Network Chips

16.5 Summary

Further Reading in Neural Network Hardware Implementation

17 Optical Neuro-Computing

17.0 Overview

17.1 Historical Introduction of Optical Neurocomputing

17.2 Review of Learning Algebras and Architectures

17.3 Associative Memory vs. Wiener Filter and Self-Organization-Map vs. Kalman Filters

17.4 Optical Implementations of Neural Networks

17.5 Comparison Between Electronic and Optic Implementations of Neural Networks

17.6 Hybrid Neurocomputing

17.7 Application to Pattern Recognition and Image Processing

17.8 The Superconducting Mechanism

17.9 The Super-Triode

17.10 The Super-Triode Neurocomputer

17.11 Wave-Front Imaging Telescope with a Focal Plane Array of Super-Triodes

17.12 Space-Borne In-Situ Smart Sensing with Neurocomputing

17.13 Conclusion


18 Neural Networks for Spatio-Temporal Pattern Recognition

18.0 Overview

18.1 Creating Spatial Analogues of Temporal Patterns

18.2 Neural Networks with Time Delays

18.3 Storing and Generating Temporal Patterns Via Recurrent Connections

18.4 Using Neurons with Time-Varying Activations and Summing Information Over Time Intervals

18.5 Neural Nets which have Short-Term and Long-Term Memories

18.6 Frequency Coding in Neural Networks

18.7 Networks with Combinations of Different Temporal Capabilities

18.8 Summary


19 Neural Networks for Medical Diagnosis

19.0 Overview

19.1 Introduction

19.2 Prospects for Neural Networks in Medicine

19.3 Potential Niches for Neural Network Diagnostic Aids

19.4 Factors Affecting Physician Acceptance

19.5 Diagnostic Network Design Considerations

19.6 Existing Neural Networks for Medical Diagnosis

19.7 Existing Neural Networks for Prognosis and Treatment

19.8 Summary


20 Neural Networks for Sonar Signal Processing

20.0 Overview

20.1 Introduction

20.2 Sonar Signal Processing Systems

20.3 Beam-Forming and Bearing Estimation

20.4 Noise Cancellation

20.5 Feature Extraction

20.6 Detection and Classification

20.7 Summary


21 Fault Diagnosis

21.0 Introduction: Making Diagnostics Work in the Real World — A Few Tricks

21.1 Overview

21.2 Techniques

21.3 Applications

21.4 Power Generation Facilities

21.5 Summary


22 Neurocontrol and Related Techniques

22.0 Overview

22.1 Introduction

22.2 The Five Basic Designs

22.3 Areas of Application

22.4 Supervised Learning and Expert Systems

22.5 Further Details on the Five Basic Designs

22.6 Robust Neuro-Identification


23 Application of Neural Networks to Robotics

23.0 Overview

23.1 Neurology Applied to Robotics

23.2 Neural Networks Applied to Robotic Tasks

23.3 Technological Considerations

23.4 Summary


24 Business with Neural Networks

24.0 Introduction

24.1 Marketing

24.2 Operations Management

24.3 Financial Analysis

24.4 Where is Accounting-Auditing?

24.5 Summary


25 Neural Networks for Data Compression and Data Fusion

25.0 Overview

25.1 Introduction

25.2 Neural Networks for Data Compression and Dimensionality Reduction

25.3 Neural Networks for Image Data Compression

25.4 Neural Network Methods for Multisource Information Correlation/Fusion


26 Data Communications

26.0 Overview

26.1 Network Management

26.2 ISDN Communications Network Control

26.3 Network Switching

26.4 Data Routing

26.5 Data Interpretation

26.6 Optical Implementations

26.7 Adaptive Filter

26.8 Quadrature Amplitude Modulation

26.9 Local and Wide Area Networks


27 Neural Networks for Man/Machine Systems

27.0 Overview

27.1 Adaptive Interfaces

27.2 Adaptive Aiding

27.3 Neural Networks to Emulate Human Performance

27.4 Neural Networks for Bioengineering

27.5 Summary


28 Capturing The Future: Neural Networks in the Year 2000 and Beyond

28.0 Introduction

28.1 Prediction 1

28.2 Prediction 2

28.3 Prediction 3

28.4 Prediction 4

28.5 Prediction 5

28.6 Prediction 6

28.7 Prediction 7

28.8 Prediction 8

28.9 Prediction 9

28.10 Prediction 10




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© Academic Press 1990
Academic Press
eBook ISBN:

About the Author

Alianna J. Maren

Craig T. Harston

Robert M. Pap

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