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Synergetics of Measurement, Prediction and Control

  • Igor Grabec
  • Wolfgang Sachse

Part of the Springer Series in Synergetics book series (SSSYN, volume 68)

Table of contents

  1. Front Matter
    Pages I-XX
  2. Igor Grabec, Wolfgang Sachse
    Pages 1-9
  3. Igor Grabec, Wolfgang Sachse
    Pages 11-50
  4. Igor Grabec, Wolfgang Sachse
    Pages 51-97
  5. Igor Grabec, Wolfgang Sachse
    Pages 99-124
  6. Igor Grabec, Wolfgang Sachse
    Pages 125-135
  7. Igor Grabec, Wolfgang Sachse
    Pages 137-165
  8. Igor Grabec, Wolfgang Sachse
    Pages 167-184
  9. Igor Grabec, Wolfgang Sachse
    Pages 185-202
  10. Igor Grabec, Wolfgang Sachse
    Pages 203-242
  11. Igor Grabec, Wolfgang Sachse
    Pages 243-275
  12. Igor Grabec, Wolfgang Sachse
    Pages 277-308
  13. Igor Grabec, Wolfgang Sachse
    Pages 309-332
  14. Igor Grabec, Wolfgang Sachse
    Pages 333-390
  15. Igor Grabec, Wolfgang Sachse
    Pages 391-400
  16. Back Matter
    Pages 429-458

About this book

Introduction

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, 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.

Keywords

Tracking Transit artificial neural networks control deterministic chaos distance feedback intelligent control measurement neural networks optimal control optimization sensor sensors stability

Authors and affiliations

  • Igor Grabec
    • 1
  • Wolfgang Sachse
    • 2
  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Theoretical and Applied MechanicsCornell UniversityIthacaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-60336-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 1997
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-64359-0
  • Online ISBN 978-3-642-60336-5
  • Series Print ISSN 0172-7389
  • Buy this book on publisher's site