Abstract
The problem of fuzzy system modeling or fuzzy model identification is generally the determination of a fuzzy model for a system or process by making use of linguistic information obtained from human experts and/or numerical information obtained from input-output numerical measurements. The former approach is known as knowledge-driven modeling while the later is known as data-driven modeling. It is also possible to integrate the two approaches for developing models of complex real systems. In this tutorial, attention is focused on building optimized fuzzy model from the available data based on relatively new identification technique viz. particle swarm optimization (PSO).
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© 2006 Springer-Verlag Berlin Heidelberg
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Aggarwal, K.K., Kumar, S., Khosla, A., Singh, J. (2006). Special Tutorial — Particle Swarms for Fuzzy Models Identification. In: Tiwari, A., Roy, R., Knowles, J., Avineri, E., Dahal, K. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36266-1_40
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DOI: https://doi.org/10.1007/978-3-540-36266-1_40
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