Feedforward Neural Network Methodology

Terrence L. Fine

ISBN 10: 0387987452 / ISBN 13: 9780387987453

Edité par Springer-Verlag New York Inc., US, 1999

A propos de cet article

This monograph provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the computationally intensive methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling. The reader is provided with the information needed to make practical use of the powerful modeling and design tool of feedforward neural networks, as well as presented with the background needed to make contributions to several research frontiers. This work is therefore of interest to those in electrical engineering, operations research, computer science, and statistics who would like to use nonlinear modeling of stochastic phenomena to treat problems of pattern classification, forecasting, signal processing, machine intelligence, and nonlinear regression. T. L. Fine is Professor of Electrical Engineering at Cornell University.

N° de réf. du vendeur LU-9780387987453

Détails bibliographiques

• Titre: Feedforward Neural Network Methodology
• Éditeur: Springer-Verlag New York Inc., US
• Date de parution: 1999
• Langue: anglais
• ISBN à 10 chiffres: 0387987452
• ISBN à 13 chiffres: 9780387987453
• Reliure: Hardback
• Édition: 1999 ed.
• État de l'article: New

A propos de ce titre

Synopsis

The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two factors have cooperated to at last provide systems engineers and statisticians with a working, prac- cal, and successful ability to routinely make accurate complex, nonlinear models of such ill-understood phenomena as physical, economic, social, and information-based time series and signals and of the patterns h- den in high-dimensional data. The models are based closely on the data itself and require only little prior understanding of the stochastic mec- nisms underlying these phenomena. Among these models, the feedforward neural networks, also called multilayer perceptrons, have lent themselves to the design of the widest range of successful forecasters, pattern clas- ?ers, controllers, and sensors. In a number of problems in optical character recognition and medical diagnostics, such systems provide state-of-the-art performance and such performance is also expected in speech recognition applications. The successful application of feedforward neural networks to time series forecasting has been multiply demonstrated and quite visibly so in the formation of market funds in which investment decisions are based largely on neural network-based forecasts of performance. The purpose of this monograph, accomplished by exposing the meth- ology driving these developments, is to enable you to engage in these - plications and, by being brought to several research frontiers, to advance the methodology itself.