An evolving type-2 neural fuzzy inference system

Authors:
Sau Wai Tung;Chai Quek;Cuntai Guan
Affiliations:
Center for Computational Intelligence, Sch. of Comp. Engineering, Nanyang Technological University, Singapore;Center for Computational Intelligence, Sch. of Comp. Engineering, Nanyang Technological University, Singapore;Institute for Infocomm Research, A*Star, Singapore
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

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Abstract

Traditional designs of neural fuzzy systems are largely userdependent whereby the knowledge to form the computational structures of the systems is provided by the user. By designing a neural fuzzy system based on experts' knowledge results in a non-varying structure of the system. To overcome the drawback of a heavily user-dependent system, self-organizing methods that are able to directly utilize knowledge from the numerical training data have been incorporated into the neural fuzzy systems to design the systems. Nevertheless, this data-driven approach is insufficient in meeting the challenges of real-life application problems with time-varying dynamics. Hence, this paper is a novel attempt in addressing the issues involved in the design for an evolving Type-2 Mamdani-type neural fuzzy system by proposing the evolving Type-2 neural fuzzy inference system (eT2FIS) - an online system that is able to fulfill the requirements of evolving structures and updating parameters to model the non-stationeries in real-life applications.