BOOK
Fiesler-96.1/IDIAP
Handbook of Neural Computation
Fiesler, Emile
Ed.
Beale, R.
Ed.
adalaine
adaptive resonance theory
application
artificial neural network
associative memory
backpropagation
bidirectional associative memory
case study
combinatorial optimization
connectionism
connectionist network
control
data compression
feedback network
feedforward network
functional-link network
fundamentals
fuzzy-neural system
hardware implementation
Hopfield network
hybrid system
image processing
LVQ
madalaine
modelling of cognitive phenomena
multilayer perceptron
neocognitron
network analysis
neural computation
neural computing
neural expert system
neural network
neural-evolutionary system
ontogenic neural network
pattern classification
perceptron
prediction
radial basis function
recurrent neural network
self-organizing feature map
signal processing
software implementation
speech processing
supervised learning
time series analysis
topology
training
unsupervised learning
The Computational Intelligence Library
1996
Institute of Physics and Oxford University Press
New York, New York
The electronic version is expected in early 1997.
0-7503-0312--3 and 0-7503-0413-8
Many scientists and engineers now use neural networks to tackle problems that are either intractable, or unrealistically time consuming to solve, through traditional computational strategies. To address the need for speedy dissemination of new ideas in this field to a broad spectrum of neural network users, designers and implementers, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to neural network fundamentals, models, algorithms, applications and implementations. This work is intended to become the standard reference resource for the neural network community. The Handbook of Neural Computation will be produced in parallel in two updatable formats, looseleaf paper and CD-ROM, and will be kept up to date by means of supplements published on a regular basis. Details of new architectures, algorithms and applications may be submitted to the Handbook editors for peer review and possible inclusion in a future supplement to the Handbook. In this way we will create a moving compendium of the state of the art of neural computation. Key features of the Handbook of Neural Computation: * A hands-on guide to the design and implementation of neural networks * A comprehensive source of reference for all neural network users, designers and implementers * Provides an information pathway between scientists and engineers in different disciplines who apply neural networks to generically similar problems * Provides timely information in a rapidly changing field