T2-HyFIS-Yager: type 2 hybrid neural fuzzy inference system realizing Yager inference

Authors:
S. W. Tung;C. Quek;C. Guan
Affiliations:
Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;Institute for Infocomm Research, A*Star, Singapore
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

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Abstract

The Hybrid neural Fuzzy Inference System (HyFIS) is a five layers adaptive neural fuzzy inference system, based on the Compositional Rule of Inference (CRI) scheme, for building and optimizing fuzzy models. To provide the HyFIS architecture with a firmer and more intuitive logical framework that emulates the human reasoning and decision-making mechanism, the fuzzy Yager inference scheme, together with the self-organizing gaussian Discrete Incremental Clustering (gDIC) technique, were integrated into the HyFIS network to produce the HyFIS-Yager-gDIC. This paper presents T2- HyFIS-Yager, a Type-2 Hybrid neural Fuzzy Inference System realizing Yager inference, for learning and reasoning with noise corrupted data. The proposed T2-HyFIS-Yager is used to perform time-series forecasting where a non-stationary timeseries is corrupted by additive white noise of known and unknown SNR to demonstrate its superiority as an effective neuro-fuzzy modeling technique.