Overview
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Full Product Details
Author: Gérard Dreyfus
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition: 2005 ed.
Dimensions:
Width: 15.50cm
, Height: 2.80cm
, Length: 23.50cm
Weight: 1.980kg
ISBN: 9783540229803
ISBN 10: 3540229809
Pages: 498
Publication Date: 03 August 2005
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: In Print
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.
Language: English
Reviews
From the reviews: Artificial neural networks (ANN) generated fascinating dreams of solving problems in complex systems ! . The present book, contributed to by several authors, provides a clear description with statistical analysis for ANN, together with examples to show the power and advantages of ANN. Comparisons of ANN to traditional statistical methods, such as linear regressions, the Bayes statistics, etc. are also dealt with. This will greatly help readers to understand the principles and to use ANN correctly to develop significant applications. (Min Ping Qian, Mathematical Reviews, Issue 2007 a) We are nowadays looking at ANNs as a machine learning tool offering a wide range of possibilities in the modeling and ordering of data, in signal processing, adaptive control, and many other fields. The book offers a systematic, thorough and understandable introduction to this field. ! the book is a useful introduction for engineers and researchers in the field of modeling, data processing, control, machine learning, optimization, and related fields. (Andreas Schierwagen, Zentralblatt MATH, Vol. 1119 (21), 2007)
