000 07300nam a2201513 i 4500
001 5263132
003 IEEE
005 20220712205627.0
006 m o d
007 cr |n|||||||||
008 100317t20152001nyua ob 001 0 eng d
020 _a9780470544037
_qelectronic
020 _z9780780353695
_qprint
020 _z0470544031
_qelectronic
024 7 _a10.1109/9780470544037
_2doi
035 _a(CaBNVSL)mat05263132
035 _a(IDAMS)0b000064810c32a6
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA76.87
_b.F54 2001eb
082 0 4 _a006.3/2
_222
245 0 2 _aA field guide to dynamical recurrent networks /
_cedited by John F. Kolen, Stefan C. Kremer.
246 3 0 _aDynamical recurrent networks
264 1 _aNew York :
_bIEEE Press,
_cc2001.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2009]
300 _a1 PDF (xxx, 421 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
500 _a"IEEE order no. PC5809"--T.p. verso.
504 _aIncludes bibliographical references (p. 379-408) and index.
505 0 _aPreface. 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.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
520 _aAcquire 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.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aDescription based on PDF viewed 12/21/2015.
650 0 _aNeural networks (Computer science)
_93414
655 0 _aElectronic books.
_93294
695 _aRecurrent neural networks
695 _aRegions
695 _aRobots
695 _aRobustness
695 _aSections
695 _aSilicon
695 _aSkeleton
695 _aStability analysis
695 _aSwitches
695 _aSyntactics
695 _aTaxonomy
695 _aTensile stress
695 _aTerminology
695 _aTime series analysis
695 _aTrademarks
695 _aTraining
695 _aTrajectory
695 _aTransfer functions
695 _aTransient analysis
695 _aTurbo codes
695 _aTuring machines
695 _aUpper bound
695 _aViterbi algorithm
695 _aWireless communication
695 _aAdaptive systems
695 _aAlgorithm design and analysis
695 _aApproximation algorithms
695 _aApproximation methods
695 _aArrays
695 _aArtificial neural networks
695 _aAutomata
695 _aBenchmark testing
695 _aBibliographies
695 _aBiographies
695 _aBiological system modeling
695 _aBooks
695 _aChaos
695 _aClustering algorithms
695 _aComputational modeling
695 _aComputer architecture
695 _aComputers
695 _aConnectors
695 _aContext
695 _aControl systems
695 _aConvolution
695 _aData preprocessing
695 _aData structures
695 _aDecoding
695 _aDelay
695 _aDelay effects
695 _aDoped fiber amplifiers
695 _aDynamics
695 _aEncoding
695 _aEquations
695 _aFeedforward neural networks
695 _aFiltering theory
695 _aFinite impulse response filter
695 _aGrammar
695 _aHeuristic algorithms
695 _aHistory
695 _aIIR filters
695 _aIndexes
695 _aKernel
695 _aKnowledge engineering
695 _aLatches
695 _aLearning systems
695 _aLogic gates
695 _aLogistics
695 _aMagnetic heads
695 _aMathematical model
695 _aMaximum likelihood detection
695 _aNatural languages
695 _aNeurons
695 _aNoise
695 _aNonlinear filters
695 _aNumerical models
695 _aOscillators
695 _aPersonal digital assistants
695 _aPolynomials
695 _aPragmatics
695 _aProposals
695 _aQuantization
695 _aReal time systems
700 1 _aKolen, John F.,
_d1965-
_926598
700 1 _aKremer, Stefan C.,
_d1968-
_926599
710 2 _aJohn Wiley & Sons,
_epublisher.
_96902
710 2 _aIEEE Xplore (Online service),
_edistributor.
_926600
776 0 8 _iPrint version:
_z9780780353695
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5263132
942 _cEBK
999 _c73823
_d73823