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

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

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

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