A Field Guide to Dynamical Recurrent Networks
Electrical Engineering A Field Guide to Dynamical Recurrent Networks 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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Dynamical Recurrent Networks
PART n ARCHITECTURES
Buffers to the Rescue
Representation of Discrete States
Simple Stable Encodings of FiniteState Machines in Dynamic
The Difficulty of Learning
Sentence Processing and Linguistic Structure
Neural Network Architectures for the Modeling of Dynamic
Looking Back and Looking
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algorithm applied attractor automaton back propagation behavior Bengio bifurcation BPTT chapter Chomsky hierarchy clusters computation connections corresponding defined delay derivatives described DFA extraction discrete discriminant function dynamical recurrent networks dynamical systems Elman embedding encoding equations error example extracted DFA feedforward networks finite finite-state automata first-order fixed point forecast fuzzy gradient gradient descent hidden units implemented initial input symbol iterated Laguerre filter layer learning algorithms limit cycle linear logistic mapping mapping Mealy machine memory methods module Moore machines NARX networks network architecture neurons node noise nonlinear oscillations output units parameters performance plant prediction problem processing recurrent neural networks regular language representation robusmess RTRL second-order Section sequence shown in Figure Siegelmann Sierpinski triangle sigmoid function signals simulation space stack step strings structure task Theorem training set trajectory transformation transition Turing machine unfolding values VC dimension vector weight update
Страница iii - Department of Computing and Information Science University of Guelph Guelph, Ontario, Canada...
Страница 394 - Department of Computer Science University of Maryland, Baltimore County, MD 21228 Dipak Ghosal, Prasad R. Chintamaneni, and Satuh K. Tvipnthi Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland College Park, MD 20742 Abstract In this paper, we present analytical models using Petri nets for fault-tolerant schemes used in distributed systems.