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Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) Hardcover – 28 Aug 2001

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Product details

  • Hardcover: 308 pages
  • Publisher: John Wiley & Sons; 1 edition (28 Aug. 2001)
  • Language: English
  • ISBN-10: 0471495174
  • ISBN-13: 978-0471495178
  • Product Dimensions: 17.3 x 2.3 x 24.7 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 4,717,948 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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From the Back Cover

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. * Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting * Examines stability and relaxation within RNNs * Presents on-line learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, data-reusing adaptation, and normalisation * Studies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration * Describes strategies for the exploitation of inherent relationships between parameters in RNNs * Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.

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Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition. Read the first page
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3 of 3 people found the following review helpful By A Customer on 21 Sept. 2001
Format: Hardcover
What can I say but WOW. This is a truly excellent book on the very complex topic of recurrent neural networks. Each chapter provides a refreshing and clear insight into otherwise baffling subject. I will certainly have this book by my side at all times during my journey through Neural Networks. A must read!
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2 of 3 people found the following review helpful By A Customer on 28 Nov. 2001
Format: Hardcover
"Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability," approaches the field of recurrent neural networks from both a practical and a theoretical perspective. Starting from the fundamentals, where unexpected insights are offered even at the level of the dynamical richness of simple neurons, the authors describe many existing algorithms and gradually introduce novel ones. The latter are convicingly shown to yield better prediction performances than traditional approaches, when applied to real-world data. They also dedicate a considerable amount of time on the (practical) issue of nonlinearity analysis of time series, which is or should be, indeed, the cradle of all proper modelling and/or filtering solutions: nonlinearity should be assessed prior to choosing the appropriate model and/or filters, since linear ones are to be preferred if sufficient for the problem. I would recommend this book to any researcher who is active in the field of recurrent neural networks and time series analysis, but also to researchers who are new in the field, since the book offers an extensive overview of the current state-of-the-art approaches.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 2 reviews
10 of 13 people found the following review helpful
Unexpected insights that make you go: "Aha!" 28 Nov. 2001
By Temujin Gautama - Published on Amazon.com
Format: Hardcover
"Recurrent Neural Networks for Prediction: Learning Algorithms,
Architectures and Stability," approaches the field of recurrent neural networks from both a practical and a theoretical perspective. Starting from the fundamentals, where unexpected insights are offered even at the level of the dynamical richness of simple neurons, the authors describe many existing algorithms and gradually introduce novel ones. The latter are convicingly shown to yield better prediction performances than traditional approaches, when applied to real-world data. They also dedicate a considerable amount of time on the (practical) issue of nonlinearity analysis of time series, which is or should be, indeed, the cradle of all proper modelling and/or filtering solutions: nonlinearity should be assessed prior to choosing the appropriate model and/or filters, since linear ones are to be preferred if sufficient for the problem. I would recommend this book to any researcher who is active in the field of recurrent neural networks and time series analysis, but also to researchers who are new in the field, since the book offers an extensive overview of the current state-of-the-art approaches.
3 of 4 people found the following review helpful
I should buy it in 2001. 19 Sept. 2007
By Valentine Dvorovkin - Published on Amazon.com
Format: Hardcover
I give this book 5 stars. It is a must have book, very well written. It has good balance between rigorous theory and authors reasonong regarding the subject.
I'm not a beginner in this field, and still I found a lot of interesting ideas, that can help not only to improve quality of the net, but also make you see "bigger picture".
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