Title :
Evolving Complex-Valued Interval Type-2 Fuzzy Inference System
Author :
K. Subramanian;S. Suresh
Author_Institution :
Air Traffic Management Research Institute, Nanyang Technological University, Singapore, 639798
Abstract :
Interval Type-2 fuzzy systems have been shown to be extremely capable of handling vagueness as well as uncertainty in data, while complex-valued fuzzy sets have been demonstrated to be capable of solving classification problems efficiently. This paper combines their collective advantage to propose a complex-valued Interval Type-2 Fuzzy Inference System (referred to as CIT2FIS). To derive the fuzzy rules, a Recursive Least Squares based algorithm is proposed. The proposed algorithm evolves (add/ prune) and adapts the rules in an evolving online fashion.
Keywords :
"Inference algorithms","Fuzzy logic","Learning systems","Fuzzy sets","Inference mechanisms","Uncertainty","Training"
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on
DOI :
10.1109/FUZZ-IEEE.2015.7338088
