Intelligent Engineering Systems and Computational Cybernetics

  • J. A. Tenreiro Machado
  • Béla Pátkai
  • Imre J. Rudas

Table of contents

  1. Front Matter
    Pages I-X
  2. Intelligent Robotics

    1. Emese Szádeczky-Kardoss, Bálint Kiss
      Pages 3-14
    2. António M. Lopes, Fernando G. Almeida
      Pages 37-47
    3. Miguel F. M. Lima, J. A. Tenreiro Machado, Manuel Crisóstomo
      Pages 49-60
  3. Artificial Intelligence

    1. Dušan Kocur, Jozef Krajňák, Stanislav Marchevský
      Pages 73-79
    2. Sandra Lovrenčić, Ivorka Jurenec Tomac, Blaženka Mavrek
      Pages 81-91
  4. Computational Intelligence

    1. Kristóf Csorba, István Vajk
      Pages 107-117
    2. Ana Meštrović, Mirko Čubrilo
      Pages 119-136
    3. Dana Klimešová
      Pages 137-142
    4. Muhammad Azizur Rahman, Algirdas Pakstas, Frank Zhigang Wang
      Pages 143-159
    5. Domonkos Tikk, Zsolt T. Kardkovács, Zoltán Bánsághi
      Pages 185-195
    6. António Ferrolho, Manuel Crisóstomo
      Pages 197-207
    7. Mohammad Reza Rajati, Hamid Khaloozadeh, Alireza Fatehi
      Pages 209-219
  5. Intelligent Mechatronics

    1. László Lemmer, Bálint Kiss
      Pages 223-233

About this book

Introduction

Engineering practice often has to deal with complex systems of multiple variable and multiple parameter models almost always with strong non-linear coupling. The conventional analytical techniques-based approaches for describing and predicting the behaviour of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction, the more work has to be invested to obtain it.

A further important component of machine intelligence is a kind of “structural uniformity” giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to the ready-to-wear industry, which introduced products, which can be slightly modified later on in contrast to tailor-made creations aiming at maximum accuracy from the beginning. These subsequent corrections can be carried out by machines automatically. This “learning ability” is a key element of machine intelligence.

The past decade confirmed that the view of typical components of the present soft computing as fuzzy logic, neural computing, evolutionary computation and probabilistic reasoning are of complementary nature and that the best results can be applied by their combined application.

Today, the two complementary branches of Machine Intelligence, that is, Artificial Intelligence and Computational Intelligence serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in Journal and conference proceedings worldwide substantiates this statement. The present book contains several articles taking different viewpoints in the field of intelligent systems.

Keywords

Computational Intelligence Evolution Norm Performance Web Services communication complex systems computational cybernetics gradient descent intelligent engineering systems knowledge modeling neural network robot robotics

Editors and affiliations

  • J. A. Tenreiro Machado
    • 1
  • Béla Pátkai
    • 2
  • Imre J. Rudas
    • 3
  1. 1.Department of Electrotechnical Engineering Rua Dr. Antonio Bernardino de AlmeidaInstitute of Engineering of the Polytechnic Institute of PortoPortoPortugal
  2. 2.Department of Engineering Institute for ManufacturingUniversity of CambridgeCambridgeUK
  3. 3.Department of Intelligent Engineering Systems John von Neumann Faculty of InformaticsBudepest TechBudapestHungary

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4020-8678-6
  • Copyright Information Springer Netherlands 2009
  • Publisher Name Springer, Dordrecht
  • eBook Packages Computer Science
  • Print ISBN 978-1-4020-8677-9
  • Online ISBN 978-1-4020-8678-6
  • About this book