A framework of adaptive T-S type Rough-Fuzzy Inference Systems (ARFIS)
Faculty of Computing, Health and Science
School of Computer and Security Science
The rough-fuzzy hybridization scheme has become of research interest in a variety of areas over the past decade. The present paper proposes a general framework for adaptive T-S type rough-fuzzy inference systems (ARFIS) for many practical applications. Rough set theory is utilized to reduce the number of attributes and to obtain a minimal set of decision rules based on input-output data sets. A T-S type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering algorithm and the rough set approach, respectively. The generated T-S type rough-fuzzy inference system is then adjusted by the least squares fit and the conjugate gradient descent algorithm towards better performance with a validity checking for the generated minimal set of rules. The proposed framework of ARFIS is able to reduce the number of rules which increases exponentially when more input variables are involved and also to assess the validity of the minimized decision rules. The performance of the proposed framework of ARFIS is compared with other existing approaches in a variety of application areas and shown to be very competitive.