Back Propagation: Theory, Architectures, and Applications

By Yves Chauvin; David E. Rumelhart | Go to book overview

BACKPROPAGATION
Theory, Architectures, and Applications

Edited by Yves Chauvin Stanford University and Net-ID, Inc. David E. Rumelhart Department of Psychology Stanford University

LAWRENCE ERLBAUM ASSOCIATES, PUBLISHERS

1995 Hillsdale, New Jersey Hove, UK

-iii-

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Back Propagation: Theory, Architectures, and Applications
Table of contents

Table of contents

  • Title Page iii
  • Contents v
  • Preface vii
  • Acknowledgments ix
  • 1: Backpropagation: The Basic Theory 1
  • Introduction 1
  • Conclusion 33
  • References 33
  • 2: Phoneme Recognition Using Time-Delay Neural Networks 35
  • Abstract 35
  • Acknowledgment 59
  • References 59
  • 3: Automated Aircraft Flare and Touchdown Control Using Neural Networks 63
  • 3: Automated Aircraft Flare and Touchdown Control Using Neural Networks 63
  • Introduction 63
  • Acknowledgments 98
  • References 98
  • 4: Recurrent Backpropagation Networks 99
  • 4: Recurrent Backpropagation Networks 99
  • Introduction 99
  • Acknowledgments 130
  • Appendix A. Gradient Evaluation 130
  • Appendix B. Stability of Error Back- Propagation Equation 132
  • Appendix C. Pseudocode 133
  • References 134
  • 5: A Focused Backpropagation Algorithm for Temporal Pattern Recognition 137
  • 5: A Focused Backpropagation Algorithm for Temporal Pattern Recognition 137
  • Introduction 137
  • Appendix: Derivation of the Focused Back- Propagation Algorithm 166
  • Acknowledgments 168
  • References 168
  • 6: Nonlinear Control with Neural Networks 171
  • 6: Nonlinear Control with Neural Networks 171
  • Introduction 171
  • Summary 187
  • Acknowledgments 188
  • References 188
  • 7: Forward Models: Supervised Learning with a Distal Teacher 189
  • Abstract 189
  • Conclusions 230
  • Acknowledgments 231
  • References 232
  • Appendix A 234
  • Appendix B 235
  • Appendix C 236
  • 8: Backpropagation: Some Comments and Variations 237
  • Introduction 237
  • References 269
  • 9: Graded State Machines: The Representation of Temporal Contingencies in Feedback Networks 273
  • 9: Graded State Machines 273
  • Introduction 274
  • Acknowledgment 311
  • References 311
  • 10: Spatial Coherence as an Internal Teacher for a Neural Network 313
  • Abstract 313
  • Introduction 314
  • Conclusions 343
  • Acknowledgments 344
  • Appendix a the Mutual Information Gradient in the Binary Case 344
  • Appendix B the Mutual Information Gradient in the Continuous Case 346
  • References 348
  • 11: Connectionist Modeling and Control of Finite State Systems Given Partial State Information 351
  • 11: Connectionist Modeling and Control of Finite State Systems Given Partial State Information 351
  • Introduction 351
  • References 386
  • 12: Backpropagation and Unsupervised Learning in Linear Networks 389
  • Introduction 389
  • Appendix: Some Mathematical Proofs 424
  • Acknowledgment 430
  • References 430
  • 13: Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity 433
  • Introduction 433
  • Acknowledgment 480
  • References 480
  • A. Appendix 482
  • 14: When Neural Networks Play Sherlock Holmes 487
  • 14: When Neural Networks Play Sherlock Holmes 487
  • Introduction 487
  • Acknowledgments 505
  • Appendix 505
  • References 507
  • 15: Gradient Descent Learning Algorithms: A Unified Perspective 509
  • 15: Gradient Descent Learning Algorithms 509
  • Introduction 509
  • Conclusion 533
  • Appendix 533
  • Acknowledgements 539
  • References 539
  • Author Index 543
  • Subject Index 549
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