Engineering Systems with Intelligence

Concepts, Tools and Applications

  • Spyros G. Tzafestas

Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)

Table of contents

  1. Front Matter
    Pages i-xv
  2. High Autonomy Systems

    1. Front Matter
      Pages 1-1
    2. B. P. Zeigler, S. D. Chi, F. E. Cellier
      Pages 3-22
  3. Knowledge Based System

    1. Front Matter
      Pages 23-23
    2. Afshin Shamsolmaali
      Pages 25-32
    3. G. A. Vouros, C. D. Spyropoulos
      Pages 43-49
    4. G Papakonstantinou, T Panayiotopoulos, M Sideri
      Pages 59-64
    5. T. Panayiotopoulos, G. Papakonstantinou
      Pages 65-71
    6. Robert P. Futrelle, Ioannis A. Kakadiaris
      Pages 73-81
    7. R. V. van de Ree, H. Koppelaar, E. J. H. Kerckhoffs
      Pages 83-90
    8. Carlos Ramos, Eugénio Oliveira
      Pages 99-106
    9. Eugenio Oliveira, Carlos Ramos
      Pages 107-115
    10. Jian Peng, Stephen Cameron
      Pages 117-124
    11. E. A. Giakoumakis, G. Papakonstantinou
      Pages 133-137
    12. G. Frangakis, P. E. Trahanias
      Pages 139-146
    13. D. A. Gaganelis, E. D. Frangoulis
      Pages 147-154

About this book

Introduction

This book contains a selection of papers presented at the "European Robotics and Intelligent Systems Conference" (EURISCON '91) held in Corfu. Greece (June 23-28. 1991). It is devoted to the analysis. design and applications of technological systems with built-in intelligence achieved through appropriate blending of mathematical, symbolic. sensing. computer processing. and feedback control concepts. methods and software / hardware tools. System intelligence includes human-like capabilities such as learning. observation. perception. interpretation. reasoning. planning. decision making. and action. Integrated intelligent decision and control systems obey Saridis' prinCiple of Increasing Precision with Decreasing Intelligence (IPDI). and have a hierarchical structure with three basic levels. namely Organization. Coordination. and Execution Levels. As we proceed from the organization to the execution level. the precision about the jobs to be completed increases and accordingly the intelligence reqUired for these jobs decreases. As an example. it is mentioned here that in an intelligent robotic system the organization tasks can be realized using a neural net. the coordination tasks by a Petri net. and the execution tasks by local sensors and actuators. The field of intelligent systems is a new interdisciplinary field with continuously increasing interest and expansion. It is actually the outcome of the synergetic interaction and cooperation of classical fields such as system theory. control theory. artificial intelligence. operational research. information theory. electronics. communications. and others.

Keywords

Fuzzy Sensor Simulation algorithms automation autonom complex system fuzzy logic image processing logic ontology production quality assurance robot robotics

Editors and affiliations

  • Spyros G. Tzafestas
    • 1
  1. 1.Department of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-011-2560-4
  • Copyright Information Kluwer Academic Publishers 1991
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-5130-9
  • Online ISBN 978-94-011-2560-4
  • Series Print ISSN 2213-8986
  • About this book
Industry Sectors
Pharma
Materials & Steel
Automotive
Chemical Manufacturing
Biotechnology
Electronics
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering