Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent part parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published based on the parameter adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods for the training of type-2 adaptive fuzzy-neuro systems are investigated: derivative-based (computational approaches), derivative-free (heuristic meth∗Corresponding author Email addresses: [email protected] (Saima Hassan), [email protected] (Mojtaba Ahmadieh Khanesar), [email protected] (Erdal Kayacan), [email protected] (Jafreezal Jaafar), [email protected] (Abbas Khosravi) Preprint submitted to Journal of LTEX Templates May 17, 2016 ods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.