Feedforward Neural Network Methodology

Terrence L. Fine

ISBN 10: 1475773099 / ISBN 13: 9781475773095

Edité par Springer New York Apr 2013, 2013

A propos de cet article

This item is printed on demand - it takes 3-4 days longer - Neuware -This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems. 360 pp. Englisch.

N° de réf. du vendeur 9781475773095

Détails bibliographiques

• Titre: Feedforward Neural Network Methodology
• Éditeur: Springer New York Apr 2013
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 1475773099
• ISBN à 13 chiffres: 9781475773095
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 20 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 20 millimètres de profondeur
• Poids: 546 grammes

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.