Introduction to Computational Intelligence Paradigms
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.
KeywordsGenetic Algorithm Fuzzy Logic Input Pattern Fuzzy Logic Controller Predicate Logic
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- 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
- Dubois, D. and Prade, H. (1992), “Putting rough sets and fuzzy sets together,” in Slowinski, R. (Ed.), Intelligent Decision Support, Kluwer, Dordrecht.Google Scholar
- 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
- 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
- Pearl, J. (1997), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA.Google Scholar
- Pedrycz, W. (1998), Computational Intelligence: an Introduction, CRC Press, Boca Raton.Google Scholar
- 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
- Shastri, L. (Guest ed.) (1994), “A fuzzy logic symposium,” IEEE Expert, vol. 9, no. 4, pp. 2–49.Google Scholar