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 ManipulationThe 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 ApproximationA Brief Survey. - References. - Author Index. Language: English