Complex-Valued neuro-fuzzy inference system based classifier

Authors:
Kartick Subramanian;Ramaswamy Savitha;Sundaram Suresh;B. S. Mahanand
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;Dept. of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

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Abstract

In this paper, we propose a complex-valued Takagi-Sugeno-Kang type-0 neuro-fuzzy inference system (CNFIS) and develop for it, a gradient-descent based learning algorithm to solve classification problems. The gradient-descent based learning algorithm is derived based on Wirtinger calculus: which preserves the amplitude-phase correlation. The performance of the developed algorithm is evaluated on a set of four binary classification problems and three multi-category classification problems. Comparison with various real-valued and complex-valued classifiers show the improved performance of CNFIS.