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A field guide to dynamical recurrent networks / edited by John F. Kolen, Stefan C. Kremer.

Contributor(s): Kolen, John F, 1965- | Kremer, Stefan C, 1968- | John Wiley & Sons [publisher.] | IEEE Xplore (Online service) [distributor.].
Material type: materialTypeLabelBookPublisher: New York : IEEE Press, c2001Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2009]Description: 1 PDF (xxx, 421 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780470544037.Other title: Dynamical recurrent networks.Subject(s): Neural networks (Computer science) | Recurrent neural networks | Regions | Robots | Robustness | Sections | Silicon | Skeleton | Stability analysis | Switches | Syntactics | Taxonomy | Tensile stress | Terminology | Time series analysis | Trademarks | Training | Trajectory | Transfer functions | Transient analysis | Turbo codes | Turing machines | Upper bound | Viterbi algorithm | Wireless communication | Adaptive systems | Algorithm design and analysis | Approximation algorithms | Approximation methods | Arrays | Artificial neural networks | Automata | Benchmark testing | Bibliographies | Biographies | Biological system modeling | Books | Chaos | Clustering algorithms | Computational modeling | Computer architecture | Computers | Connectors | Context | Control systems | Convolution | Data preprocessing | Data structures | Decoding | Delay | Delay effects | Doped fiber amplifiers | Dynamics | Encoding | Equations | Feedforward neural networks | Filtering theory | Finite impulse response filter | Grammar | Heuristic algorithms | History | IIR filters | Indexes | Kernel | Knowledge engineering | Latches | Learning systems | Logic gates | Logistics | Magnetic heads | Mathematical model | Maximum likelihood detection | Natural languages | Neurons | Noise | Nonlinear filters | Numerical models | Oscillators | Personal digital assistants | Polynomials | Pragmatics | Proposals | Quantization | Real time systemsGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.3/2 Online resources: Abstract with links to resource Also available in print.
Contents:
Preface. Acknowledgments. List of Figures. List of Tables. List of Contributors. INTRODUCTION. Dynamical Recurrent Networks (J. Kolen and S. Kremer). ARCHITECTURES. Networks with Adaptive State Transitions (D. Calvert and S. Kremer). Delay Networks: Buffers to Rescue (T. Lin and C. Giles). Memory Kernels (A. Tsoi, et al.). CAPABILITIES. Dynamical Systems and Iterated Function Systems (J. Kolen). Representation of Discrete States (C. Giles and C. Omlin). Simple Stable Encodings of Finite-State Machines in Dynamic Recurrent Networks (M. Forcada and R. Carrasco). Representation Beyond Finite States: Alternatives to Pushdown Automata (J. Wiles, et al.). Universal Computation and Super-Turing Capabilities (H. Siegelmann). ALGORITHMS. Insertion of Prior Knowledge (P. Frasconi, et al.). Gradient Calculations for Dynamic Recurrent Neural Networks (B. Pearlmutter). Understanding and Explaining DRN Behavior (C. Omlin). LIMITATIONS. Evaluating Benchmark Problems by Random Guessing (J. Schmidhuber, et al.). Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies (S. Hochreiter, et al.. Limiting the Computational Power of Recurrent Neural Networks: VC Dimension and Noise (C. Moore). APPLICATIONS. Dynamical Reccurent Networks in Control (D. Prokhorov, et al.). Sentence Processing and Linguistic Structure (W. Tabor). Neural Network Architectures for the Modeling of Dynamic Systems (H. Zimmerman and R. Neuneier). From Sequences to Data Structures: Theory and Applications (P. Frasconi, et al.). CONCLUSION. Dynamical Recurrent Networks: Looking Back and Looking Forward (S. Kremer and J. Kolen). Bibliography. Glossary. Index. About the Editors.
Summary: Acquire the tools for understanding new architectures and algorithms of dynamical recurrent networks (DRNs) from this valuable field guide, which documents recent forays into artificial intelligence, control theory, and connectionism. This unbiased introduction to DRNs and their application to time-series problems (such as classification and prediction) provides a comprehensive overview of the recent explosion of leading research in this prolific field. A Field Guide to Dynamical Recurrent Networks emphasizes the issues driving the development of this class of network structures. It provides a solid foundation in DRN systems theory and practice using consistent notation and terminology. Theoretical presentations are supplemented with applications ranging from cognitive modeling to financial forecasting. A Field Guide to Dynamical Recurrent Networks will enable engineers, research scientists, academics, and graduate students to apply DRNs to various real-world problems and learn about different areas of active research. It provides both state-of-the-art information and a road map to the future of cutting-edge dynamical recurrent networks.
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"IEEE order no. PC5809"--T.p. verso.

Includes bibliographical references (p. 379-408) and index.

Preface. Acknowledgments. List of Figures. List of Tables. List of Contributors. INTRODUCTION. Dynamical Recurrent Networks (J. Kolen and S. Kremer). ARCHITECTURES. Networks with Adaptive State Transitions (D. Calvert and S. Kremer). Delay Networks: Buffers to Rescue (T. Lin and C. Giles). Memory Kernels (A. Tsoi, et al.). CAPABILITIES. Dynamical Systems and Iterated Function Systems (J. Kolen). Representation of Discrete States (C. Giles and C. Omlin). Simple Stable Encodings of Finite-State Machines in Dynamic Recurrent Networks (M. Forcada and R. Carrasco). Representation Beyond Finite States: Alternatives to Pushdown Automata (J. Wiles, et al.). Universal Computation and Super-Turing Capabilities (H. Siegelmann). ALGORITHMS. Insertion of Prior Knowledge (P. Frasconi, et al.). Gradient Calculations for Dynamic Recurrent Neural Networks (B. Pearlmutter). Understanding and Explaining DRN Behavior (C. Omlin). LIMITATIONS. Evaluating Benchmark Problems by Random Guessing (J. Schmidhuber, et al.). Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies (S. Hochreiter, et al.. Limiting the Computational Power of Recurrent Neural Networks: VC Dimension and Noise (C. Moore). APPLICATIONS. Dynamical Reccurent Networks in Control (D. Prokhorov, et al.). Sentence Processing and Linguistic Structure (W. Tabor). Neural Network Architectures for the Modeling of Dynamic Systems (H. Zimmerman and R. Neuneier). From Sequences to Data Structures: Theory and Applications (P. Frasconi, et al.). CONCLUSION. Dynamical Recurrent Networks: Looking Back and Looking Forward (S. Kremer and J. Kolen). Bibliography. Glossary. Index. About the Editors.

Restricted to subscribers or individual electronic text purchasers.

Acquire the tools for understanding new architectures and algorithms of dynamical recurrent networks (DRNs) from this valuable field guide, which documents recent forays into artificial intelligence, control theory, and connectionism. This unbiased introduction to DRNs and their application to time-series problems (such as classification and prediction) provides a comprehensive overview of the recent explosion of leading research in this prolific field. A Field Guide to Dynamical Recurrent Networks emphasizes the issues driving the development of this class of network structures. It provides a solid foundation in DRN systems theory and practice using consistent notation and terminology. Theoretical presentations are supplemented with applications ranging from cognitive modeling to financial forecasting. A Field Guide to Dynamical Recurrent Networks will enable engineers, research scientists, academics, and graduate students to apply DRNs to various real-world problems and learn about different areas of active research. It provides both state-of-the-art information and a road map to the future of cutting-edge dynamical recurrent networks.

Also available in print.

Mode of access: World Wide Web

Description based on PDF viewed 12/21/2015.

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