A Field Guide to Dynamical Recurrent Networks 1st Edition by John Kolen, Stefan Kremer ISBN 0780353692 978-0780353695

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Product details: 

ISBN 10: 0780353692 

ISBN 13: 978-0780353695

Author:  John F. Kolen,  Stefan C. Kremer

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.

Table of contents: 

PART I INTRODUCTION

Chapter 1 Dynamical Recurrent Networks

John F. Kolen and Stefan C. Kroner
1.1 Introduction
1.2 Dynamical Recurrent Networks
1.3 Overview
1.4 Conclusion
PART II ARCHITECTURES

Chapter 2 Networks with Adaptive State Transitions

David Calvert and Stefan C. Kremer
2.1 Introduction
2.2 The Search for Context
2.3 Recurrent Approaches to Context
2.4 Representing Context
2.5 Training
2.6 Architectures
2.7 Conclusion
Chapter 3 Delay Networks Buffers to the Rescue

Tsung-Nan Lin and C. Lee Giles
3.1 Introduction to Delay Networks
3.2 Back-Propagation Through Time Learning Algorithm
3.3 Delay Networks with Feedback NARX Networks
3.4 Long-Term Dependencies in NARX Networks
3.5 Experimental Results The Latching Problem
3.6 Conclusion
Chapter 4 Memory Kernels

Ah Chung Tsoi, Andrew Back, Jose Principe, and Mike Mozer
4.1 Introduction
4.2 Different Types of Memory Kernels
4.3 Generic Representation of a Memory Kernel
4.4 Basis Issues
4.5 Universal Approximation Theorem
4.6 Training Algorithms
4.7 Illustrative Example
4.8 Conclusion
PART III CAPABILITIES

Chapter 5 Dynamical Systems and Iterated Function Systems

John F. Kolen
5.1 Introduction
5.2 Dynamical Systems
5.3 Iterated Function Systems
5.4 Symbolic Dynamics
5.5 The DRN Connection
5.6 Conclusion
Chapter 6 Representation of Discrete States

C. Lee Giles and Christian Omlin
6.1 Introduction
6.2 Finite-State Automata
6.3 Neural Network Representations of DFA
6.4 Pushdown Automata
6.5 Turing Machines
6.6 Conclusion

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John Kolen,Stefan Kremer,A Field,Dynamical Recurrent,