| Description |
1 online resource (xxx, 421 pages) : illustrations |
| Contents |
Introduction -- Dynamical recurrent networks / John F. Kolen and Stefan C. Kremer -- Architectures -- Networks with adaptive state transitions / David Calvert and Stefan C. Kremer -- Delay networks: buffers to the rescue / Tsung-Nan Lin and C. Lee Giles -- Memory Kernels / Ah Chung Tsoi, Andrew Back, José Principe, and Mike Mozer -- Capabilities -- Dynamical systems and iterated function systems / John F. Kolen -- Representation of discrete states / C. Lee Giles and Christian Omlin -- Simple stable encodings of finite-state machines in dynamic recurrent networks / Mikel L. Forcada and Raphael C. Carrasco -- Representation beyond finite states: alternatives to pushdown automata / Janet Wiles, Alan D. Blair, and Mikael Bodén -- Universal computation and super-turing capabilities / Hava T. Siegelmann -- Algorithms -- Insertion of prior knowledge / Paolo Frasconi [and others] -- Gradient calculations for dynamic recurrent neural networks / Barak A. Pearlmutter -- Understanding and explaining DRN behavior / Christian Omlin -- Limitations -- Evaluating benchmark problems by random guessing / Jürgen Schmidhuber, Sepp Hochreiter, and Yoshua Bengio -- Gradient flow in recurrent nets: the difficulty of learning long-term dependencies / Sepp Hochreiter [and others] -- Limiting the computational power of recurrent neural networks: VC dimension and noise / Christopher Moore -- Applications -- Dynamical recurrent networks in control / Danil V. Prokhorov, Gintaras V. Puskorius, and Lee A. Feldkamp -- Sentence processing and linguistic structure / Whitney Tabor -- Neural network architectures for the modeling of dynamic system / Hans-Georg Zimmermann and Ralph Neuneier -- From sequences to data structures: theory and applications / Paolo Frasconi [and others] -- Conclusion -- Dynamical recurrent networks: looking back and looking forward / Stefan C. Kremer and John F. Kolen |
| 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 |
| Bibliography |
Includes bibliographical references (pages 379-408) and index |
| Notes |
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL |
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Print version record |
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digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL |
| Subject |
Neural networks (Computer science)
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Neural Networks, Computer
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Neural networks (Computer science)
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Rekursives neuronales Netz
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| Form |
Electronic book
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| Author |
Kolen, John F., 1965-
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Kremer, Stefan C., 1968-
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| ISBN |
9780470544037 |
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0470544031 |
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9780790853697 |
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0790853698 |
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