Recent Contributions in Intelligent Systems

  • Vassil Sgurev
  • Ronald R. Yager
  • Janusz Kacprzyk
  • Krassimir T. Atanassov

Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Table of contents

  1. Front Matter
    Pages i-x
  2. Samuel Delepoulle, André Bigand, Christophe Renaud, Olivier Colot
    Pages 1-22
  3. Stefka Fidanova, Miroslav Shindarov, Pencho Marinov
    Pages 33-44
  4. Petia Georgieva, Luis Alberto Paz Suárez, Sebastião Feyo de Azevedo
    Pages 45-64
  5. Tomohiro Hara, Tielong Shen, Yasuhiko Mutoh, Yinhua Liu
    Pages 65-83
  6. Tatjana Kolemishevska-Gugulovska, Mile Stankovski, Imre J. Rudas, Nan Jiang, Juanwei Jing
    Pages 85-102
  7. Maciej Krawczak, Sotir Sotirov, Evdokia Sotirova
    Pages 103-113
  8. Patrick Person, Thierry Galinho, Hadhoum Boukachour, Florence Lecroq, Jean Grieu
    Pages 205-227
  9. Simeon Ribagin, Vihren Chakarov, Krassimir Atanassov
    Pages 229-247
  10. Anthony G. Shannon, Beloslav Riecan, Evdokia Sotirova, Krassimir Atanassov, Maciej Krawczak, Pedro Melo-Pinto et al.
    Pages 263-277
  11. Maria Stefanova-Pavlova, Velin Andonov, Todor Stoyanov, Maia Angelova, Glenda Cook, Barbara Klein et al.
    Pages 279-290
  12. Yancho Todorov, Margarita Terziyska, Michail Petrov
    Pages 291-312

About this book


This volume is a brief, yet comprehensive account of new development, tools, techniques and solutions in the broadly perceived “intelligent systems”. New concepts and ideas concern the development of effective and efficient models which would make it possible to effectively and efficiently describe and solve processes in various areas of science and technology. Special emphasis is on the dealing with uncertainty and imprecision that permeates virtually all real world processes and phenomena, and has to properly be modeled by formal and algorithmic tools and techniques so that they be adequate and useful. The papers in this volume concern a wide array of possible techniques exemplified by, on the one hand, logic, probabilistic, fuzzy, intuitionistic fuzzy, neuro-fuzzy, etc. approaches. On the other hand, they represent the use of such systems modeling tools as generalized nets, optimization and control models, systems analytic models, etc. They concerns a variety of approaches, from pattern recognition, image analysis, education system modeling, biological and medical systems modeling, etc.


Intelligent Systems developments Probabilistic Approach Intuitionistic Fuzzy Approach Neuro-fuzzy Approach Generalized Nets Pattern Recognition Image Analysis

Editors and affiliations

  • Vassil Sgurev
    • 1
  • Ronald R. Yager
    • 2
  • Janusz Kacprzyk
    • 3
  • Krassimir T. Atanassov
    • 4
  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Machine Intelligence Institute, Hagan School of BusinessIona CollegeNew RochelleUSA
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  4. 4.Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-41437-9
  • Online ISBN 978-3-319-41438-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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