Introduction to Computational Intelligence Paradigms

  • Z. Chen
  • A. M. Fanelli
  • G. Castellano
  • L. C. Jain
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 62)


Computational intelligence techniques involve the use of computers to enable machines to simulate human performance. The prominent paradigms used include AI systems, artificial neural networks, multimedia, fuzzy logic, evolutionary computing techniques, artificial life, computer vision, adaptive intelligence, and chaos engineering. These knowledge-based computational intelligence techniques have generated tremendous interest among scientists and application engineers due to a number of benefits such as generalization, adaptation, fault tolerance and self-repair, self-organization and evolution. Successful demonstration of the applications of knowledge-based systems theories will aid scientists and engineers in finding sophisticated and low cost solutions to difficult problems. This chapter provides a simple introduction to computational intelligence paradigms.


Genetic Algorithm Fuzzy Logic Input Pattern Fuzzy Logic Controller Predicate Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Chen, Z. (1999), Computational Intelligence for Decision Support, CRC Press, Boca Raton, FL.CrossRefGoogle Scholar
  2. [2]
    Cordon, O., Herrera, F., and Lozano, M. (1997), “A classified review on the combination fuzzy logic-genetic algorithms bibliography: 1989–1995,” in Sanchez, E., Shibata, T., and Zadeh, L.A. (Eds.), Genetic Algorithms and Fuzzy Logic Systems, pp. 209–240.Google Scholar
  3. [3]
    Dubois, D. and Prade, H. (1992), “Putting rough sets and fuzzy sets together,” in Slowinski, R. (Ed.), Intelligent Decision Support, Kluwer, Dordrecht.Google Scholar
  4. [4]
    Jain, L.C. (Ed.) (2000), Innovative Teaching and Learning: Knowledge-Based Paradigms, Springer-Verlag, Heidelberg.MATHGoogle Scholar
  5. [5]
    Jain, L.C. (Ed.) (1997), Soft Computing Techniques in Knowledge-Based Intelligent Engineering Systems, Springer-Verlag, Heidelberg.MATHGoogle Scholar
  6. [6]
    Kappen, B. and Gielen, C. (1995), “Neural networks: artificial intelligence and industrial applications, Proc. of Third Annual SNN Symp. on Neural Networks, Springer, London.CrossRefGoogle Scholar
  7. [7]
    Katayama, R., Kuwata, K., and Jain, L.C. (1996), “Fusion technology of neuro, fuzzy, genetic and chaos theory and its applications,” Hybrid Intelligent Engineering Systems, World Scientific Publishing Company, Singapore, pp. 167–186.Google Scholar
  8. [8]
    Kasabov, N.K. (1996), Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, Cambridge, MA, 1996.MATHGoogle Scholar
  9. [9]
    Pawlak, Z. (1997), “Rough sets present state and further prospects,” in Wang, P.P. (Ed.), Advances in Machine Intelligence & Soft-Computing, vol. IV, pp. 4–16.Google Scholar
  10. [10]
    Pearl, J. (1997), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA.Google Scholar
  11. [11]
    Pedrycz, W. (1998), Computational Intelligence: an Introduction, CRC Press, Boca Raton.Google Scholar
  12. [12]
    Sanchez, E., Shibata, T., and Zadeh, L.A. (Eds.) (1997), Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives, World Scientific, Singapore.MATHGoogle Scholar
  13. [13]
    Schwefel, H.-P. and Back, T. (1998), “Artificial evolution: how and why?” Chapter 1 in Uagliarela, D., Periaux, J., Poloni, C., and Winter, G. ( Eds. ), Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, pp. 1–20.Google Scholar
  14. [14]
    Shastri, L. (Guest ed.) (1994), “A fuzzy logic symposium,” IEEE Expert, vol. 9, no. 4, pp. 2–49.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • Z. Chen
  • A. M. Fanelli
  • G. Castellano
  • L. C. Jain

There are no affiliations available

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