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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Format: | eBook |
Language: | English |
Published: |
New York, NY
Springer US
1969, 1969
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Edition: | 1st ed. 1969 |
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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