FLC Modeling of Classical EEG Signals Model
using the Technique of TSK - Fuzzy Inference
Rules and its Generalization

Prakash N. Kamble

doi:https://doi.org/10.14445/22315373/IJMTT-V21P504

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 21 | Number 1 | Year 2015 | Article Id. IJMTT-V21P504 | DOI : https://doi.org/10.14445/22315373/IJMTT-V21P504

FLC Modeling of Classical EEG Signals Model using the Technique of TSK - Fuzzy Inference Rules and its Generalization

Prakash N. Kamble

Abstract

In this paper we use, the idea of “modeling of a model”. That is, we reform the classical mathematical model of EEG signals model to fuzzy model utilizing the modeling technique of Takagi-Sugeno-Kang (TSK) fuzzy rule base.. We design the model using exactly same inputs and their values (sensor readings) as that of used in designing classical mathematical model of EEG signal and achieve the desired output result. Further we generalize this model by making variations in the input sensor readings and also achieve expected output results. Further it is to be noted that the efforts required to work out the fuzzy model are more feasible as compare to that of the classical mathematical model of EEG signals.

Keywords

Mathematical model of EEG signals, I/Ps-O/P linguistic variables, Mamdani fuzzy inference rules, TSK fuzzy inference rules, weighted average formula.

References

(1) George J. Klir and Bo Yuan, Fuzzy sets and fuzzy logic (Theory and applications), PHI, New Delhi, 2006.
(2) John Yen and Reza Langari, Fuzzy Logic (Intelligence, Control and Information), Pearson Education, 1999.
(3) Kamble P.N., Mherotra S.C and Dhakne M.B., Superiority Of Fuzzy Controllers over Mathematical Modeling of EEG Signals, Proceedings Of The International Conference On Mathematical Sciences In Honour of A. M. Mathai, pp. 175- 190, January- 2011.
(4) Kamble P.N., Mherotra S.C and Dhakne M.B., TSK - fuzzy Controlled Modeling via Classical Mathematical EEG Signals Modeling, International Journal of Mathematical Archive, PP.147-154, October, 2013.
(5) Kazuo Tanaka, An introduction to fuzzy logic for practical applications, Springer, 1997.
(6) Metin Akay, Editor, Nonlinear Biomedical Signal processing Volume - I (Fuzzy Logic, Neural Networks and New Algorithms), IEEE press products, 2000
(7) Saeid Sanei and J. A. Chambers, EEG signal processing, John Wiley and sons Ltd, 2007.
(8) Simulator for Neural Networks and Action potentials (SNNAP) Tutorial, The University of Texas-Houston Medical School, 2003,http//snap.uth.tmc.edu.
(9) Terrance J. Senijnowski Towardas, Brain computer Interfacing, The MIT Press Cambridge, London, England, 2007.

Citation :

Prakash N. Kamble, "FLC Modeling of Classical EEG Signals Model using the Technique of TSK - Fuzzy Inference Rules and its Generalization," International Journal of Mathematics Trends and Technology (IJMTT), vol. 21, no. 1, pp. 31-41, 2015. Crossref, https://doi.org/10.14445/22315373/IJMTT-V21P504