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Advances in Information Systems Science Volume 1

Engineering has long been thought of by the public as a profession tra­ ditionally categorized into such branches as electrical, mechanical, chemical, industrial, civil, etc. This classification has served its purpose for the past half century; but the last decade has witnessed a tremendous change....

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Bibliographic Details
Main Author: Tou, Julius T.
Format: eBook
Language:English
Published: New York, NY Springer US 1969, 1969
Edition:1st ed. 1969
Subjects:
Humanities And Social Sciences
Humanities
Social Sciences
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Theory of Algorithms and Discrete Processors
  • 1. Introduction
  • 2. Discrete Processors
  • 3. Examples of Discrete Processors
  • 4. Computers and Discrete Processors
  • 5. Systems of Algorithmic Algebras
  • 6. Application of Algorithmic Algebras to Transformations of Microprograms
  • 7. Equivalence of Discrete Processors
  • 8. Equivalence of Automata with Terminal States Relative to an Automaton without Cycles
  • 9. Specific Cases of Solutions to the Equivalence Problem
  • 10. Conclusions
  • References
  • 2 Programming Languages
  • 1. Introduction
  • 2. The Basic Linguistic Nature of Programming Languages
  • 3. Programming Languages and Semiotics
  • 4. The Formal Definition of Programming Lan guages
  • 5. The Definition of Programmable Automata and their Languages
  • 6. Parallel Concurrent Processes
  • 7. Machine Languages
  • 8. Special and General-Purpose Algorithmic Languages
  • 9. Special Problem-Oriented Languages
  • 10. Simulation Languages
  • 11. Conversational Languages
  • 12. Conclusion
  • References
  • 3 Formula Manipulation—The User’s Point of View
  • 1. Introduction
  • 2. Different Types of Formula Manipulation Systems
  • 3. Toward a Mathematical Utility
  • 4. The Formula Manipulation Language Symbal
  • 5. The Syntax of Symbal
  • 6. The Basic Symbols and Syntactic Entities
  • 7. Expressions
  • 8. The Remaining Parts of the Language
  • 9. Standard Variables
  • 10. Techniques and Applications
  • 11. Summary
  • References
  • 4 Engineering Principles of Pattern Recognition
  • 1. Introduction
  • 2. Basic Problems in Pattern Recognition
  • 3. Feature Selection and Preprocessing
  • 4. Pattern Classification by Distance Functions
  • 5. Pattern Classification by Potential Functions..
  • 6. Pattern Classification by Likelihood Functions
  • 7. Pattern Classification by Entropy Functions..
  • 8. Conclusions.-References
  • 5 Learning Control Systems
  • 1. Introduction
  • 2. Trainable Controllers
  • 3. Reinforcement Learning Control Systems
  • 4. Bayesian Learning in Control Systems
  • 5. Learning Control Systems Using Stochastic Approximation
  • 6. The Method of Potential Functions and its Application to Learning Control
  • 7. Stochastic Automata as Models of Learning Controllers
  • 8. Conclusions
  • Appendix. Stochastic Approximation—A Brief Survey
  • References
  • Author Index

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