A new book "Neural Network Parallel Computing"

Yoshiyasu Takefuji yxt3 at po.CWRU.Edu
Sat Nov 23 17:54:03 EST 1991


NEW FROM KLUWER ACADEMIC PUBLISHERS

Neural Network Parallel Computing

By Yoshiyasu Takefuji, Case Western Reserve University, USA
1992 ISBN 0-7923-9190-X Cloth 240 pages $65.00

Neural Network Parallel Computing is the first book and the only single
book available for the professional on neural network computing for
optimization problems. This introductory book is for experts in a
variety of areas including parallel computing, neural network computing,
computer science, communications, graph theory, computer aided design
for VLSI circuits, molecular biology, management science, and operations
research, as well as for the novice in these areas.

Neural Network Parallel Computing provides real applications and
real-world examples. The computational power of neural network computing
is demonstrated by solving numerous problems such as N-queen, crossbar
switch scheduling, four-coloring and k-colorability, graph planarization
and channel routing, RNA secondary structure prediction, knight's tour,
spare allocation, sorting and searching, and tiling.

Neural Network Parallel Computing presents a major breakthrough in
science as well as a variety of engineering fields. This book is an
excellent reference for researchers in all areas covered in this book.
This text may also be used as a senior or graduate level course on the
topic.



Contents

Preface
Acknowledgements

Chapter 1       Neural network models and N-queen problems
        1.1 Introduction
        1.2 Mathematical neural network models
        1.3 N-queen neural network 
        1.4 General optimization programs
        1.5 N-queen simulation programs 
        1.6 References
        1.7 Exercises

Chapter 2       Crossbar switch scheduling problems
        2.1 Introduction
        2.2 Crossbar scheduling problems and N-queen problems
        2.3 References
        2.4 Exercises

Chapter 3       Four-coloring map problems and k-colorability problems
        3.1 Introduction
        3.2 Four-coloring neural network
        3.3 K-colorability neural network
        3.4 References
        3.5 Exercises

Chapter 4       Graph planarization problems
        4.1 Introduction
        4.2 Neural representation and motion equations
        4.3 References
        4.4 Exercises

Chapter 5       Channel routing problems
        5.1 Introduction
        5.2 Graph planarization and channel routing
        5.3 References
        5.4 Exercises

Chapter 6       RNA secondary structure prediction
        6.1 Introduction
        6.2 Maximum independent set problems
        6.3 Predicting the secondary structure in ribonucleic acids
        6.4 Graph planarization and RNA secondary structure prediction
        6.5 References
        6.6 Exercises

Chapter 7       Knight's tour problems
        7.1 Introduction
        7.2 Neural representation and motion equations
        7.3 References
        7.4 Exercises

Chapter 8       Spare Allocation problems
        8.1 Introduction
        8.2 Neural representation and motion equations
        8.3 References
        8.4 Exercises
             
Chapter 9       Sorting and Searching
        9.1 Introduction
        9.2 Sorting
        9.3 Searching
        9.4 References

Chapter 10      Tiling problems
        10.1 Introduction
        10.2 Neural representation and motion equations
        10.3 References
        10.4 Exercises

Chapter 11      Silicon neural networks
        11.1 Introduction
        11.2 Analog implementations
        11.3 Digital implementations
        11.4 References
        11.5 Exercises

Chapter 12      Mathematical background of the artificial neural network
        12.1 Introduction and four neuron models
        12.2 Why is the decay term harmful?
        12.3 Basic analog convergence theorem and proof
        12.4 Discrete sigmoid neural network convergence theorem and proof
        12.5 McCulloch-Pitts neural network convergence theorem and proof
        12.6 Hysteresis McCulloch-Pitts neural network convergence theorem 
        and proof
        12.7 Maximum neural network convergence theorem and proof
        12.8 Other neuron models
        12.9 References

Chapter 13      Forthcoming applications
        13.1 Introduction
        13.2 Time slot assignment in TDM hiearchical switching system
        13.3 Broadcast scheduling in packet radio networks
        13.4 Module orientation problems
        13.5 Maximum clique problems

Chapter 14      Conjunctoids and  artificial learning
        14.1 Introduction
        14.2 Multinomial conjunctoid concepts
        14.3 Multnomial conjunctoid circuitry
        14.4 References
             
Subject index


To order the book by mail
Kluwer Academic Publishers
Order Department
P.O. Box 358 Accord Station
Hingham, MA 02018-0358

Credit Card Customers call: (617)871-6600
Fast Convenient Service: Fax (617)871-6528 or
Email:kluwer at world.std.com

The book will be available from January 1992.



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