Front Cover; Advances in Computers, Volume 37; Copyright Page; Contents; Contributors; Preface; Chapter 1. Approaches to Automatic Programming; 1. Introduction; 2. What Does the User See?; 3. HowDoes the System Work?; 4. What Does the System Know?; 5. The Programmer's Apprentice; 6. Conclusions; References; Chapter 2. Digital Signal Processing; 1. Introduction; 2. An Historical Overview; 3. Sampling and Aliasing; 4. The Discrete Fourier Transform; 5. The Fast Fourier Transform; 6. Digital Filtering; 7. Statistical Signal Processing and Filter Design; 8. Programmable DSP's

9. Software Design Tools10. Closure; References; Chapter 3. Neural Networks for Pattern Recognition; 1. Introduction; 2. Neural Network Architectures; 3. Gradient Descent Learning Algorithms; 4. Relaxation Learning Algorithms; 5. Hebbian Learning; 6. Performance and Implementation; 7. Conclusions; References; Chapter 4. Experiments in Computational Heuristics and Their Lessons for Software and Knowledge Engineering; 1. Overview; 2. The Nature of Heuristic Computation; 3. Every Interactive System Evolves into Hyperspace : The Case of the Smart Game Board

4. A Snapshot of Two Olympic Medalists: Players for Go and Nine Men's Morris5. Exhaustive Search in Large Spaces: The Complexity of Jigsaw Puzzles; 6. Exploratory Software Development : Uncertain Resources, Open-Ended Goals; 7. The Unpredictable Complexity of Knowledge Formalization; 8. Conclusions; Acknowledgments; References; Chapter 5. High-Level Synthesis of Digital Circuits; 1. Introduction; 2. Circuit Modeling; 3. Compilation and Behavioral Optimization; 4. Structural Synthesis; 5. Scheduling; 6. Data-Path Synthesis and Optimization; 7. Control Synthesis

8. Synthesis of Pipelined Circuits9. High-Level Synthesis Systems; 10. Conclusions; Acknowledgments; References; Chapter 6. Issues in Dataflow Computing; 1. Introduction; 2. Dataflow Principles; 3. Earlier Dataflow Architectures; 4. A Survey of Current Dataflow Proposals; 5. Data Structures; 6. Program Allocation; 7. Resource Requirements in Dataflow; 8. Conclusion; References; Chapter 7. A Sociological History of the Neural Network Controversy; 1. Introduction : A Sociological View of Scientific Controversies; 2. The Controversy of the Perceptron; 3. The Problems of Early Neural Networks

4. Training Multilayer Networks : A "Reverse Salient" of Neural5. Interpretative Flexibility; 6. Closure of the Controversy 1 : Widrow's Group; 7. Closure of the Controversy 2: The SRI Group; 8. Closure of the Controversy 3: Rosenblatt; 9. The 1980s: A Changing Context; 10. History of Back-Propagation; 11. Back-Propagation: Learning in Multilayer Perceptrons; 12. The Neural Network Explosion; 13. The Current Debate: Conclusions; Appendix 1 . List of Those Interviewed; Appendix 2 . List of Personal Communications by Letter; Abbreviations Used; Acknowledgments; References; Author Index

Subject Index

ADVANCES IN COMPUTERS VOL 37

Description based upon print version of record.