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International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605
Volume 7, Issue 2, October 2013 pp 52-57
Research Article
FLC Modeling of Classical EEG Signals Model by the Technique Tsukamoto Fuzzy Rule Base
Kamble P. N.
Department of Mathematics, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, Maharashtra, INDIA.
Academic Editor: Dr. Dase R. K.
In this paper we consider a system (plant) of “Hodgkin-Hoxley classical mathematical model of EEG signals" as an input-output map We assume that the internal structure of this system is unknown, but qualitative knowledge about the behavior is available in the form of “If - Then" rules. We construct a mathematical description of the system, based on available information, so that it will represents faithfully the true system of “Tsukamoto Fuzzy Control Model". The construction process consists of translating linguistic rules into mathe-matical expression using fuzzy sets and fuzzy logic with the technique of Tsukamoto fuzzy inference rules so that desired output (o/p) result is achieved. In essence Tsukamoto Fuzzy Controlled Model is constructed by fusing multiple local models that associated with fuzzy subspaces of the given inputs (I/Ps) space. These I/Ps are nothing but I/Ps of the classical EEG signal model. Furthermore the set of fuzzy I/Ps subspaces form a fuzzy decomposition of the I/Ps space. Finally the result of fusing multiple local models (in terms of Fuzzy “If - Then" rules) by the technique of Tsukamoto Fuzzy Rule Base method gives a final output result which is equivalent to the final output result of classical EEG signal model. The obtained fuzzy controlled system is shown to be within the class of designs capable of approximating the true input - output relation to the required degree of accuracy.
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