Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives by Simon Ha

Description: FREE SHIPPING UK WIDE Nonlinear Dynamical Systems by Simon Haykin, Irwin W. Sandberg, James T. Lo, José C. Principe, Craig L. Fancourt, Shigeru Katagiri This book deals with a specialized part of neural networks having applications in control, signal processing and time series analysis. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection strengths known as synaptic weights. In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses: * Classification problems and the related problem of approximating dynamic nonlinear input-output maps * The development of robust controllers and filters * The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error * Segmenting a time series It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries. Back Cover The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes–through a learning process and information storage involving interconnection strengths known as synaptic weights. In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses: Classification problems and the related problem of approximating dynamic nonlinear input-output maps The development of robust controllers and filters The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error Segmenting a time series It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries. Flap The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection strengths known as synaptic weights. In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses: * Classification problems and the related problem of approximating dynamic nonlinear input-output maps * The development of robust controllers and filters * The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error * Segmenting a time series It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries. Author Biography IRWIN W. SANDBERG is a chaired professor at the University of Texas at Austin. JAMES T. LO teaches in the Department of Mathematics and Statistics, University of Maryland. CRAIG L. FANCOURT is a member of the Adaptive Image and Signal Processing Group at the Sarnoff Corp. in Princeton, New Jersey. JOSE C. PRINCIPE is BellSouth Professor in the Electrical and Computer Engineering Department at the University of Florida, Gainesville. SHIGERU KATAGIRI leads research on speech and hearing at NTT Communication Science Laboratories, Kyoto, Japan. SIMON HAYKIN teaches at McMaster University in Hamilton, Ontario, Canada. He has authored or coauthored over a dozen Wiley titles. Table of Contents Preface. Feedforward Neural Networks: An Introduction (S. Haykin). Uniform Approximation and Nonlinear Network Structures (I. Sandberg). Robust Neural Networks (J. Lo). Modeling, Segmentation, and Classification of Nonlinear Nonstationary Time Series (C. Fancourt & J. Principe). Application of Feedforward Networks to Speech (S. Katagiri). Index. Review "…an interesting book, useful for researchers in network theory…" (Dynamical Systems Magazine, July 2006) Long Description The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection strengths known as synaptic weights. In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses: ∗ Classification problems and the related problem of approximating dynamic nonlinear input-output maps ∗ The development of robust controllers and filters ∗ The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error ∗ Segmenting a time series It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries. Review Text "?an interesting book, useful for researchers in network theory?" (Dynamical Systems Magazine, July 2006) Review Quote "...an interesting book, useful for researchers in network theory..." ( Dynamical Systems Magazine , July 2006) Promotional "Headline" "...an interesting book, useful for researchers in network theory..." (Dynamical Systems Magazine, July 2006) Feature The first book to deal with this topic in book form. The authors are well known published experts on the topic. Details ISBN0471349119 Author Shigeru Katagiri Short Title NONLINEAR DYNAMICAL SYSTEMS Pages 312 Language English ISBN-10 0471349119 ISBN-13 9780471349112 Media Book Format Hardcover Illustrations Yes Year 2001 Subtitle Feedforward Neural Network Perspectives Place of Publication New York Country of Publication United States Residence Ontario, -CN Birth 1931 Edition 1st DOI 10.1604/9780471349112 Series Number 21 UK Release Date 2001-01-23 AU Release Date 2001-02-07 NZ Release Date 2001-02-07 Publisher John Wiley & Sons Inc Series Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Publication Date 2001-01-23 Imprint Wiley-Interscience DEWEY 629.836 Audience Undergraduate US Release Date 2001-01-23 We've got this At The Nile, if you're looking for it, we've got it. 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