The Handbook of Neural Computation.
E. Fiesler
efiesler at idiap.ch
Thu Oct 10 10:45:17 EDT 1996
----------------------------- PLEASE POST ---------------------------------
Announcing
the
H A N D B O O K O F N E U R A L C O M P U T A T I O N
___________________________________________________________
The first of three volumes in the Computational Intelligence Library
http://www.oup-usa.org/acadref/compint.html
http://www.oup-usa.org/acadref/honc.html
___________________________________________
The Handbook of Neural Computation is now available for purchase from
Oxford University Press and Institute of Physics Publishing. This
major new resource for the neural computing community offers a wealth
of information on neural network fundamentals, models, hardware and
software implementations, and applications. The handbook includes many
detailed case studies describing successful applications of artificial
neural networks in application areas such as perception and cognition,
engineering, physical sciences, biology and biochemistry, medicine,
economics, finance and business, computer science, and the arts and
humanities.
One of the unique features of this handbook is that it has been
designed to remain up to date: as neural network models, imple-
mentations, and applications continue to develop, the handbook
will keep pace by publishing new articles and revisions to exist-
ing articles. The print edition of the handbook consists of 1,100
A4-size pages published in loose-leaf format, which will be updated
by means of supplements published every six months. The electronic
edition, to be launched in January 1997 but now available for
advance purchase, includes the complete content of the handbook
on CD-ROM, plus integrated access to the latest version of the
handbook's content on the World Wide Web. Hence the handbook
combines inherent updatability with the latest modes of distribution.
The Handbook of Neural Computation is itself part of a larger
project called the Computational Intelligence Library, which
includes companion handbooks in evolutionary and fuzzy computation.
Print Edition: October 1996. 9x12 inches (230x305mm).
Four-post binder expands to accommodate supplements.
1,096 pages, 400 illustrations, ISBN 0-7503-0312-3.
Electronic Edition: January 1997. CD-ROM plus World Wide Web Access.
ISBN 0-7503-0411-1.
Further information, including details of a special introductory
price offer valid until the end of 1996, may be obtained at:
http://www.oup-usa.org/acadref/honc.html
and
http://www.oup-usa.org/acadref/compint.html
or by sending e-mail or regular mail to:
Peter Titus
Oxford University Press
198 Madison Avenue
New York, NY 10016-4314
Fax: (1) 212-726-6442
E-mail: pkt at oup-usa.org
TABLE OF CONTENTS
Preface
Russell Beale and Emile Fiesler
Foreword
James A Anderson
How to Use This Handbook
PART A INTRODUCTION
A1 Neural Computation: The Background
A1.1 The historical background
J G Taylor
A1.2 The biological and psychological background
Michael A Arbib
A2 Why Neural Networks?
Paul J Werbos
A2.1 Summary
A2.2 What is a neural network?
A2.3 A traditional roadmap of artificial neural network capabilities
PART B FUNDAMENTAL CONCEPTS OF NEURAL COMPUTATION
B1 The Artificial Neuron
Michael A Arbib
B1.1 Neurons and neural networks: the most abstract view
B1.2 The McCulloch-Pitts neuron
B1.3 Hopfield networks
B1.4 The leaky integrator neuron
B1.5 Pattern recognition
B1.6 A note on nonlinearity and continuity
B1.7 Variations on a theme
B2 Neural Network Topologies
Emile Fiesler
B2.1 Introduction
B2.2 Topology
B2.3 Symmetry and asymmetry
B2.4 High order topologies
B2.5 Fully connected topologies
B2.6 Partially connected topologies
B2.7 Special topologies
B2.8 A formal framework
B2.9 Modular topologies
Massimo de Francesco
B2.10 Theoretical considerations for choosing a network topology
Maxwell B Stinchcombe
B3 Neural Network Training
James L Noyes
B3.1 Introduction
B3.2 Characteristics of neural network models
B3.3 Learning rules
B3.4 Acceleration of training
B3.5 Training and generalization
B4 Data Input and Output Representations
Thomas O Jackson
B4.1 Introduction
B4.2 Data complexity and separability
B4.3 The necessity of preserving feature information
B4.4 Data preprocessing techniques
B4.5 A 'case study' review
B4.6 Data representation properties
B4.7 Coding schemes
B4.8 Discrete codings
B4.9 Continuous codings
B4.10 Complex representation issues
B4.11 Conclusions
B5 Network Analysis Techniques
B5.1 Introduction
Russell Beale
B5.2 Iterative inversion of neural networks and its applications
Alexander Linden
B5.3 Designing analyzable networks
Stephen P Luttrell
B6 Neural Networks: A Pattern Recognition Perspective
Christopher M Bishop
B6.1 Introduction
B6.2 Classification and regression
B6.3 Error functions
B6.4 Generalization
B6.5 Discussion
PART C NEURAL NETWORK MODELS
C1 Supervised Models
C1.1 Single-layer networks
George M Georgiou
C1.2 Multilayer perceptrons
Luis B Almeida
C1.3 Associative memory networks
Mohamad H Hassoun and Paul B Watta
C1.4 Stochastic neural networks
Harold Szu and Masud Cader
C1.5 Weightless and other memory-based networks
Igor Aleksander and Helen B Morton
C1.6 Supervised composite networks
Christian Jutten
C1.7 Supervised ontogenic networks
Emile Fiesler and Krzysztof J Cios
C1.8 Adaptive logic networks
William W Armstrong and Monroe M Thomas
C2 Unsupervised Models
C2.1 Feedforward models
Michel Verleysen
C2.2 Feedback models
Gail A Carpenter (C2.2.1), Stephen Grossberg (C2.2.1, C2.2.3),
and Peggy Israel Doerschuk (C2.2.2)
C2.3 Unsupervised composite networks
Cris Koutsougeras
C2.4 Unsupervised ontogenetic networks
Bernd Fritzke
C3 Reinforcement Learning
S Sathiya Keerthi and B Ravindran
C3.1 Introduction
C3.2 Immediate reinforcement learning
C3.3 Delayed reinforcement learning
C3.4 Methods of estimating V and Q
C3.5 Delayed reinforcement learning methods
C3.6 Use of neural and other function approximators in
reinforcement learning
C3.7 Modular and hierarchical architectures
PART D HYBRID APPROACHES
D1 Neuro-Fuzzy Systems
Krzysztof J Cios and Witold Pedrycz
D1.1 Introduction
D1.2 Fuzzy sets and knowledge representation issues
D1.3 Neuro-fuzzy algorithms
D1.4 Ontogenic neuro-fuzzy F-CID3 algorithm
D1.5 Fuzzy neural networks
D1.6 Referential logic-based neurons
D1.7 Classes of fuzzy neural networks
D1.8 Induced Boolean and core neural networks
D2 Neural-Evolutionary Systems
V William Porto
D2.1 Overview of evolutionary computation as a mechanism for
solving neural system break design problems
D2.2 Evolutionary computation approaches to solving problems
in neural computation
D2.3 New areas for evolutionary computation research in neural
systems
PART E NEURAL NETWORK IMPLEMENTATIONS
E1 Neural Network Hardware Implementations
E1.1 Introduction
Timothy S Axelrod
E1.2 Neural network adaptations to hardware implementations
Perry D Moerland and Emile Fiesler
E1.3 Analog VLSI implementation of neural networks
Eric A Vittoz
E1.4 Digital integrated circuit implementations
Valeriu Beiu
E1.5 Optical implementations
I Saxena and Paul G Horan
PART F APPLICATIONS OF NEURAL COMPUTATION
F1 Neural Network Applications
F1.1 Introduction
Gary Lawrence Murphy
F1.2 Pattern classification
Thierry Den*ux
F1.3 Combinatorial optimization
Soheil Shams
F1.4 Associative memory
James Austin
F1.5 Data compression
Andrea Basso
F1.6 Image processing
John Fulcher
F1.7 Speech processing
Kari Torkkola
F1.8 Signal processing
Shawn P Day
F1.9 Control
Paul J Werbos
PART G NEURAL NETWORKS IN PRACTICE: CASE STUDIES
G1 Perception and Cognition
G1.1 Unsupervised segmentation of textured images
Nigel M Allinson and Hu Jun Yin
G1.2 Character recognition
John Fulcher
G1.3 Handwritten character recognition using neural networks
Thomas M Breuel
G1.4 Improved speech recognition using learning vector quantization
Kari Torkkola
G1.5 Neural networks for alphabet recognition
Mark Fanty, Etienne Barnard and Ron Cole
G1.6 A neural network for image understanding
Heggere S Ranganath, Govindaraj Kuntimad and John L Johnson
G1.7 The application of neural networks to image segmentation and
way-point identification
James Austin
G2 Engineering
G2.1 Control of a vehicle active suspension model using adaptive
logic networks
William W Armstrong and Monroe M Thomas
G2.2 ATM network control by neural network
Atsushi Hiramatsu
G2.3 Neural networks to configure maps for a satellite communication
network
Nirwan Ansari
G2.4 Neural network controller for a high-speed packet switch
M Mehmet Ali and Huu Tri Nguyen
G2.5 Neural networks for optimal robot trajectory planning
Dan Simon
G2.6 Radial basis function network in design and manufacturing of
ceramics
Krzysztof J Cios, George Y Baaklini, Laszlo Berke and Alex Vary
G2.7 Adaptive control of a negative ion source
Stanley K Brown, William C Mead, P Stuart Bowling and
Roger D Jones
G2.8 Dynamic process modeling and fault prediction using artificial
neural networks
Barry Lennox and Gary A Montague
G2.9 Neural modeling of a polymerization reactor
Gordon Lightbody and George W Irwin
G2.10 Adaptive noise canceling with nonlinear filters
Wolfgang Knecht
G2.11 A concise application demonstrator for pulsed neural VLSI
Alan F Murray and Geoffrey B Jackson
G2.12 Ontogenic CID3 algorithm for recognition of defects in glass
ribbon
Krzysztof J Cios
G3 Physical Sciences
G3.1 Neural networks for control of telescope adaptive optics
T K Barrett and D G Sandler
G3.2 Neural multigrid for disordered systems: lattice gauge theory
as an example
Martin Bker, Gerhard Mack and Marcus Speh
G3.3 Characterization of chaotic signals using fast learning
neural networks
Shawn D Pethel and Charles M Bowden
G4 Biology and Biochemistry
G4.1 A neural network for prediction of protein secondary structure
Burkhard Rost
G4.2 Neural networks for identification of protein coding regions
in genomic DNA sequences
E E Snyder and Gary D Stormo
G4.3 A neural network classifier for chromosome analysis
Jim Graham
G4.4 A neural network for recognizing distantly related protein
sequences
Dmitrij Frishman and Patrick Argos
G5 Medicine
G5.1 Adaptive logic networks in rehabilitation of persons with
incomplete spinal cord injury
Aleksandar Kostov, William W Armstrong, Monroe M Thomas and
Richard B Stein
G5.2 Neural networks for diagnosis of myocardial disease
Hiroshi Fujita
G5.3 Neural networks for intracardiac electrogram recognition
Marwan A Jabri
G5.4 A neural network to predict lifespan and new metastases in
patients with renal cell cancer
Craig Niederberger, Susan Pursell and Richard M Golden
G5.5 Hopfield neural networks for the optimum segmentation of
medical images
Riccardo Poli and Guido Valli
G5.6 A neural network for the evaluation of hemodynamic variables
Tom Pike and Robert A Mustard
G6 Economics, Finance and Business
G6.1 Application of self-organizing maps to the analysis of
economic situations
F Blayo
G6.2 Forecasting customer response with neural networks
David Bounds and Duncan Ross
G6.3 Neural networks for financial applications
Magali E Azema*Barac and A N Refenes
G6.4 Valuations of residential properties using a neural network
Gary Grudnitski
G7 Computer Science
G7.1 Neural networks and human-computer interaction
Alan J Dix and Janet E Finlay
G8 Arts and Humanities
G8.1 Distinguishing literary styles using neural networks
Robert A J Matthews and Thomas V N Merriam
G8.2 Neural networks for archaeological provenancing
John Fulcher
PART H THE NEURAL NETWORK RESEARCH COMMUNITY
H1 Future Research in Neural Computation
H1.1 Mathematical theories of neural networks
Shun-ichi Amari
H1.2 Neural networks: natural, artificial, hybrid
H John Caulfield
H1.3 The future of neural networks
J G Taylor
H1.4 Directions for future research in neural networks
James A Anderson
List of Contributors
Index
__________________________________________________________________________
Emile Fiesler, Editor-in-Chief of the Handbook of Neural Computation
Research Director
IDIAP E-mail: HoNC at IDIAP.CH
C.P. 592
CH-1920 Martigny WWW-URL: http://www.idiap.ch/nn.html
Switzerland ftp ftp.idiap.ch:/pub/papers/neural/README
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