A Field Guide to Dynamical Recurrent Networks

Front Cover
John F. Kolen, Stefan C. Kremer
John Wiley & Sons, Jan 15, 2001 - Computers - 421 pages
0 Reviews
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.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Dynamical Recurrent Networks
3
PART n ARCHITECTURES
13
Buffers to the Rescue
27
Memory Kernels
39
CAPABILITIES
55
Representation of Discrete States
83
Simple Stable Encodings of FiniteState Machines in Dynamic
103
Alternatives
129
PARTV LIMITATIONS
229
The Difficulty of Learning
237
APPLICATIONS
255
Sentence Processing and Linguistic Structure
291
Neural Network Architectures for the Modeling of Dynamic
311
19
351
Looking Back and Looking
377
Glossary
409

Universal Computation and Superluring Capabilities
143
ALGORITHMS
153
Gradient Calculations for Dynamic Recurrent Neural Networks
179
Understanding and Explaining DRN Behavior
207
Index
415
About the Editors
423
Copyright

Other editions - View all

Common terms and phrases

Popular passages

Page iii - Department of Computing and Information Science University of Guelph Guelph, Ontario, Canada...
Page 387 - Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution," IEEE Transactions on Neural Networks, voL 6, no.
Page 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.

About the author (2001)

About the Editors John F. Kolen has explored the computational capabilities of dynamical recurrent networks on a wide range of projects: computer tomography of ballistic tests, autonomous science on extraterrestrial sensor platforms, and laser marksmanship modeling. His research interests include neural networks, distributed processing, philosophy of computation, and computer gaming. Dr. Kolen is a member of the Institute for Human and Machine Cognition at the University of West Florida.
Stefan C. Kremer's research interests include connectionist networks (the subject of his 1996 thesis "A Theory of Grammatical Induction in the Connectionist Paradigm"), genetic algorithms, signal processing, grammar induction, and image processing. He is an assistant professor of computing and information science at the University of Guelph, Ontario, Canada, and is a founding member of the Guelph Natural Computation Research Group.

Bibliographic information