Synergetics of Measurement, Prediction and Control

In this monograph, a statistical description of natural phenomena is used to develop an information processing system capable of modeling non-linear relationships between sensory data. The system, based on self-organized, optimal preservation of empirical information, applies these relationships for prediction and adaptive control.This monograph is written for students, scientists and engineers in academia and industry who are interested in experimental work related to the adaptive modeling of natural laws, the development of sensory-neural networks, intelligent control, synergetics and informatics. No specific knowledge of advanced mathematics is presupposed. Examples taken from physics, engineering, medicine and economics demonstrate the applicability of such intelligent systems. TOC:Contents: 1. Introduction.- 2. A Quantitative Description of Nature.- 3. Transducers.- 4. Probability Densities.- 5. Information.- 6. Maximum-Entropy Principles.- 7. Adaptive Modeling of Natural Laws.- 8. Self-Organization and Formal Neurons.- 9. Empirical Modeling by Non-Parametric Regression.- 10. Linear Modeling and Invariances.- 11. Modeling and Forecasting of Chaotic Processes.- 12. Modeling by Neural Networks.- 13. Fundamentals of Intelligent Control.- 14. Self-Control in Evolution of Biological Organisms.- A. Fundamentals of Probability and Statistics.- B. Fundamentals of Deterministic Chaos.- Subject Index