Neural network learning and expert systems / Stephen I. Gallant.

Format:

Book

Author/Creator:

Gallant, Stephen I.

Language:

English

Subjects (All):

Neural networks (Computer science).

Expert systems (Computer science).

Physical Description:

1 online resource (xvi, 365 pages) : illustrations.

Place of Publication:

Cambridge, Mass. : MIT Press, ©1993.

Language Note:

English

Summary:

"Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more. In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN. The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs."

Contents:

Introduction and important definitions

Representation issues

Perceptron learning and the pocket algorithm

Winner-take-all groups or linear machines

Autoassociators and one-shot learning

Mean squared error (MSE) algorithms

Unsupervised learning

The distributed method and radial basis functions

Computational learning theory and the BRD algorithm

Constructive algorithms

Backpropagation

Backpropagation : variations and applications

Simulated annealing and Boltzmann machines

Expert systems and neural networks

Details of the MACIE system

Noise, redundancy, fault detection, and Bayesian decision theory

Extracting rules from networks.

Notes:

Includes bibliographical references (pages [349]-359) and index.

"A Bradford Book."

OCLC-licensed vendor bibliographic record.

ISBN:

9780262273404

9780262527897

0262527898

9780585040288

0585040281

OCLC:

43474728