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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks Hardback - 1999

by Reed, Russell D

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  • Title Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
  • Author Reed, Russell D
  • Binding Hardback
  • Edition Reprint
  • Condition Used - Good
  • Pages 352
  • Volumes 1
  • Language ENG
  • Publisher MIT Press (MA), U.S.A
  • Publication date 1999-03
  • Illustrated Yes
  • Bookseller's Inventory # 0262181908.G
  • ISBN 9780262181907 / 0262181908
  • Weight 1.79 lbs (0.81 kg)
  • Dimensions 9.36 x 7.25 x 1.03 in (23.77 x 18.42 x 2.62 cm)
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Category Computers - Communications / Networking
  • Library of Congress subjects Neural networks (Computer science)
  • Library of Congress Catalogue Number 98013416
  • Dewey Decimal Code 006.32
  • Quantity available 1

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From the publisher

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition).

This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.

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