Abstract
Traditional designs of neural fuzzy systems are largely user-dependent 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.
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Tung, S.W., Quek, C., Guan, C. (2010). An Evolving Type-2 Neural Fuzzy Inference System. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_49
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DOI: https://doi.org/10.1007/978-3-642-15246-7_49
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