Description
Book SynopsisAcquire 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 p
Table of Contents
Preface xvii
Acknowledgments xix
List of Figures xxi
List of Tables xxvii
List of Contributors xxix
PART I INTRODUCTION 1
Chapter 1 Dynamical Recurrent Networks 3
John F, Kolen and Stefan C. Kroner
1.1 Introduction 3
1.2 Dynamical Recurrent Networks 4
1.3 Overview 6
1.4 Conclusion 11
PART II ARCHITECTURES 13
Chapter 2 Networks with Adaptive State Transitions 15
David Calvert and Stefan C. Kremer
2.1 Introduction 15
2.2 The Search for Context 15
2.3 Recurrent Approaches to Context 17
2.4 Representing Context 18
2.5 Training 19
2.6 Architectures 19
2.7 Conclusion 25
Chapter 3 Delay Networks: Buffers to the Rescue 27
Tsung-Nan Lin and C. Lee Giles
3.1 Introduction to Delay Networks 27
3.2 Back-Propagation Through Time Learning Algorithm 28
3.3 Delay Networks with Feedback: NARX Networks 31
3.4 Long-Term Dependencies in NARX Networks 33
3.5 Experimental Results: The Latching Problem 36
3.6 Conclusion 38
Chapter 4 Memory Kernels 39
Ah Chung Tsoi, Andrew Back, Jose Principe, and Mike Mozer
4.1 Introduction 39
4.2 Different Types of Memory Kernels 40
4.3 Generic Representation of a Memory Kernel 44
4.4 Basis Issues 45
4.5 Universal Approximation Theorem 47
4.6 Training Algorithms 48
4.7 Illustrative Example 51
4.8 Conclusion 54
PART III CAPABILITIES 55
Chapter 5 Dynamical Systems and Iterated Function Systems 57
John F. Kolen
5.1 Introduction 57
5.2 Dynamical Systems 57
5.3 Iterated Function Systems 72
5.4 Symbolic Dynamics 78
5.5 The DRN Connection 80
5.6 Conclusion 81
Chapter 6 Representation of Discrete States 83
C. Lee Giles and Christian Omlin
6.1 Introduction 83
6.2 Finite-State Automata 83
6.3 Neural Network Representations of DFA 85
6.4 Pushdown Automata 99
6.5 Turing Machines 101
6.6 Conclusion 102
Chapter 7 Simple Stable Encodings of Finite-State Machines in Dynamic Recurrent Networks 103
Mikel L. Forcada and Raphael C. Carrasco
7.1 Introduction 103
7.2 Definitions 106
7.3 Encoding 109
7.4 Encoding of Mealy Machines in DRN 114
7.5 Encoding of Moore Machines in DRN 123
7.6 Encoding of Deterministic Finite-State Automata in DRN 125
7.7 Conclusion 126
7.8 Acknowledgments 127
Chapter 8 Representation Beyond Finite States: Alternatives to Pushdown Automata 129
Janet Wiles, Alan D. Blair, and Mikael Boden
8.1 Introduction 129
8.2 Hierarchies of Languages and Machines 130
8.3 DRNs and Nonregular Languages 134
8.4 Generalization and Inductive Bias 141
8.5 Conclusion 142
Chapter 9 Universal Computation and Super-Hiring Capabilities 143
Hava T. Siegelmann
9.1 Introduction 143
9.2 The Model 144
9.3 Preliminary: Computational Complexity 145
9.4 Summary of Results 146
9.5 Pondering Real Weights 149
9.6 Analog Computation 149
9.7 Conclusion 150
9.7 Acknowledgments 151
PART IV ALGORITHMS 153
Chapter 10 Insertion of Prior Knowledge 155
Paolo Frasconi, C. Lee Giles, Marco Gori, and Christian Omlin
10.1 Introduction 155
10.2 Constrained Nondeterministic Insertion in First-Order Networks 156
10.3 Second-Order Networks 160
10.4 Other Related Techniques 175
10.5 Conclusion 177
Chapter 11 Gradient Calculations for Dynamic Recurrent Neural Networks 179
Barak A. Pearlmutter
11.1 Introduction 179
11.2 Learning in Networks with Fixed Points 182
11.3 Computing the Gradient Without Assuming a Fixed Point 188
11.4 Some Simulations 196
11.5 Stability and Perturbation Experiments 198
11.6 Other Non-Fixed Point-Techniques 199
11.7 Learning with Scale Parameters 203
11.8 Conclusion 203
Chapter 12 Understanding and Explaining DRN Behavior 207
Christian Omlin
12.1 Introduction 207
12.2 Performance Deterioration 208
12.3 Dynamic Space Exploration 209
12.4 DFA Extraction: Fool's Gold? 215
12.5 Theoretical Foundations 216
12.6 How Can DFA Outperform Networks? 218
12.7 Alternative Extraction Methods 220
12.8 Extension to Fuzzy Automata 225
12.9 Application to Financial Forecasting 226
12.10 Conclusion 227
PART V LIMITATIONS 229
Chapter 13 Evaluating Benchmark Problems by Random Guessing 231
Jiirgen Schmidhuber, Sepp Hochreiter, and Yoshua Bengio
13.1 Introduction 231
13.2 Random Guessing (RG) 231
13.3 Experiments 232
13.4 Final Remarks 234
13.5 Conclusion 235
13.6 Acknowledgments 235
Chapter 14 Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies 237
Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jiirgen Schmidhuber
14.1 Introduction 237
14.2 Exponential Error Decay 237
14.3 Dilemma: Avoiding Aradient Decay Prevents Long-Term Latching 240
14.4 Remedies 241
14.5 Conclusion 243
Chapter 15 Limiting the Computational Power of Recurrent Neural Networks: VC Dimension and Noise 245
Christopher Moore
15.1 Introduction 245
15.2 Time-Bounded Networks and VC Dimension 246
15.3 Robustness to Noise 250
15.4 Conclusion 254
15.5 Acknowledgments 254
PART VI APPLICATIONS 255
Chapter 16 Dynamical Recurrent Networks in Control 257
Danil V Prokhorov, Gintaras V Puskorius, and Lee A. Feldkamp
16.1 Introduction 257
16.2 Description and Execution of TLRNN 258
16.3 Elements of Training 260
16.4 Basic Approach to Controller Synthesis 266
16.5 Example 1 272
16.6 Example 2 282
16.7 Conclusion 288
Chapter 17 Sentence Processing and Linguistic Structure 291
Whitney Tabor
17.1 Introduction 291
17.2 Case Studies: Dynamical Networks for Sentence Processing 295
17.3 Conclusion 308
Chapter 18 Neural Network Architectures for the Modeling of Dynamic Systems 311
Hans-Georg Zimmermann and Ralph Neuneier
18.1 Introduction and Overview 311
18.2 Modeling Dynamic Systems by Feedforward Neural Networks 312
18.3 Modeling Dynamic Systems by Recurrent Neural Networks 321
18.4 Combining State-Space Reconstruction and Forecasting 334
18.5 Conclusion 350
Chapter 19 From Sequences to Data Structures: Theory and Applications 351
Paolo Frasconi, Marco Gori, Andreas Kuchler, and Alessandro Sperduti
19.1 Introduction 351
19.2 Historical Remarks 352
19.3 Adaptive Processing of Structured Information 354
19.4 Applications 366
19.5 Conclusion 374
PART VII CONCLUSION 375
Chapter 20 Dynamical Recurrent Networks: Looking Back and Looking Forward 377
Stefan C. Kremer and John F. Kolen
20.1 Introduction 377
20.2 The Challenges 377
20.3 The Potential 378
20.4 The Approaches 378
20.5 The Successes 378
20.6 Conclusion 378
Bibliography 379
Glossary 409
Index 415
About the Editors 423
