Synergetics of Measurement, Prediction and Control
The electronic processing of information permits the construction of intelligent systems capable of carrying out a synergy of autonomous measurement, the modeling of natural laws, the control of processes, and the prediction or forecasting of a large variety of natural phenomena. In this monograph,...
| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1997, 1997
|
| Edition: | 1st ed. 1997 |
| Series: | Springer Series in Synergetics
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 8.5 Numerical Examples of Self-Organized Adaptation
- 8.6 Formal Neurons and the Self-Organization Process
- 9. Modeling by Non-Parametric Regression
- 9.1 The Problem of an Optimal Prediction
- 9.2 Parzen’s Window Approach to General Regression
- 9.3 General Regression Modeler, Feedback and Recognition
- 9.4 Application of the General Regression Modeler
- 10. Linear Modeling and Invariances
- 10.1 Relation Between Parametric Modeling and Invariances
- 10.2 Generalized Linear Regression Model
- 10.3 Sequential Adaptation of Linear Regression Model
- 10.4 Transition from the Cross- to Auto-Associator
- 11. Modeling and Forecasting of Chaotic Processes
- 11.1 Modeling of Chaotic Processes
- 11.2 Examples of Chaotic Process Forecasting
- 11.3 Forecasting of Chaotic Acoustic Emission Signals
- 11.4 Empirical Modeling of Non-Autonomous Chaotic Systems
- 11.5 Cascade Modeling of Chaos Generators
- 12. Modeling by Neural Networks
- B. Fundamentals of Deterministic Chaos
- B.1 Instability of Chaotic Systems
- B.2 Characterization of Strange Attractors
- B.3 Experimental Characterization of Chaotic Phenomena
- References
- 12.1 From Biological to Artificial Neural Networks
- 12.2 A Linear Associator
- 12.3 Multi-layer Perceptrons and Back-Propagation Learning
- 12.4 Radial Basis Function Neural Networks
- 12.5 Equivalence of a Radial Basis Function NN and Perceptrons
- 13. Fundamentals of Intelligent Control
- 13.1 Introduction
- 13.2 Basic Tasks of Intelligent Control
- 13.3 The Tracking Problem
- 13.4 Cloning
- 13.5 An Empirical Approach to Optimal Control
- 14. Self-Control and Biological Evolution
- 14.1 Modeling of Natural Phenomena by Biological Systems
- 14.2 Joint Modeling of Organism and Environment
- 14.3 An Operational Description of Consciousness
- 14.4 The Fundamental Problem of Evolution
- A. Fundamentals of Probability and Statistics
- A.1 Sample Points, SampleSpace, Events and Relations
- A.2 Probability
- A.3 Random Variables and Probability Distributions
- A.4 Averages and Moments
- A.5 Random Processes
- A.6 Sampling, Estimation and Statistics
- 5.4 Relative Information
- 5.5 Information Measure of Distance Between Distributions
- 6. Maximum Entropy Principles
- 6.1 Gibbs Maximum Entropy Principle
- 6.2 The Absolute Maximum Entropy Principle
- 6.3 Quantization of Continuous Probability Distributions
- 7. Adaptive Modeling of Natural Laws
- 7.1 Probabilistic Modeler of Natural Laws
- 7.2 Optimization of Adaptive Modeler Performance
- 7.3 Stochastic Approach to Adaptation Laws
- 7.4 Stochastic Adaptation of a Vector Quantizer
- 7.5 Perturbation Method of Adaptation
- 7.6 Evolution of an Optimal Modeler and Perturbation Method
- 7.7 Parametric Versus Non-Parametric Modeling
- 8. Self-Organization and Formal Neurons
- 8.1 Optimal Storage of Empirical Information in Discrete Systems
- 8.2 Adaptive Vector Quantization and Topological Mappings
- 8.3 Self-Organization Based on the Absolute Maximum-Entropy Principle
- 8.4 Derivation of a Generalized Self-Organization Rule
- 1. Introduction
- 1.1 Goal
- 1.2 Relation to Other Scientific Fields
- 1.3 Plan of the Monograph
- 2. A Quantitative Description of Nature
- 2.1 Synergetics of Natural Phenomena
- 2.2 A Description of Nature
- 2.3 Fundamentals of Quantitative Description
- 2.4 Fundamentals of Physical Laws
- 2.5 The Random Character of Physical Variables
- 2.6 Expression of Natural Laws by Differential Equations
- 2.7 Methods of Empirical Modeling
- 2.8 Introduction to Modeling by Neural Networks
- 3. Transducers
- 3.1 The Role of Sensors and Actuators
- 3.2 Sensors and Actuators of Biological Systems
- 3.3 Operational Characteristics of Transducers
- 3.4 Fabricated Transducers
- 3.5 Transducers in Intelligent Measurement Systems
- 3.6 Future Directions in Transducer Evolution
- 4. Probability Densities
- 4.1 Estimation of Probability Density
- 5. Information
- 5.1 Some Basic Ideas
- 5.2 Entropy of Information
- 5.3 Properties of Information Entropy