@article {64769,
	title = {new type-2 fuzzy modelling and identification for electrophysiological signals: a comparison between PSO, BBO, FA and GA approaches},
	journal = {Int. J. Modelling, Identification and ControlInt. J. Modelling, Identification and Control},
	volume = {29},
	year = {2018},
	month = {2018},
	pages = {163-184},
	abstract = {In this investigation a novel type-2 fuzzy model for electrophysiological signals is presented. It is based on interval type-2 fuzzy systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserve the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first is the primary model, which represents an ordinary model with a low resolution for the electrophysiological signal under consideration, the second is a fuzzy sub-model called the error model, which represents uncertainty in the primary model. Identification is achieved by innovative metaheuristic optimisation algorithms. The method{\textquoteright}s effectiveness is evaluated through testing on synthetic and real ECG signals. In addition, a detailed comparative study with several benchmark methods will be given. Intensive computer experimentations confirm that the proposed method can significantly improve convergence and resolution.},
	author = {OUALI, Mohammed-Assam and Ghanai, Mouna and CHAFAAA, Kheireddine}
}
