Neural Networks: Methodology and Applications / Edition 1

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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 described 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.
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Editorial Reviews

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"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)

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Product Details

  • ISBN-13: 9783540229803
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 9/13/2005
  • Edition description: 2005
  • Edition number: 1
  • Pages: 498
  • Product dimensions: 9.21 (w) x 6.14 (h) x 1.13 (d)

Meet the Author

Table of Contents

Neural Networks: An Overview.- Modeling with Neural Networks: Principles and Model Design Methodology.- Modeling Methodology: Dimension Reduction and Re-sampling Methods.- Neural Identification of Controlled Dynamical Systems and Recurrent Networks.- Closed-Loop Control Learning.- Discrimination.- Self-organizing Maps and Unsupervised Classification.- Neural Networks without Training for Optimization.

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