HyFIS-Yager-gDIC: a self-organizing hybrid neural fuzzy inference system realizing Yager inference

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:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

The Hybrid neural Fuzzy Inference System (HyFIS) is a five layers adaptive neural fuzzy system for building and optimizing fuzzy models. In this paper, the fuzzy Yager inference scheme, which accounts for a firm and intuitive logical framework that emulates the human reasoning and decision-making mechanism, is integrated into the HyFIS network. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is used to form the fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters needed in each input and output dimensions. The proposed self-organizing Hybrid neural Fuzzy Inference System based on Yager inference (HyFIS-Yager-gDIC) is benchmarked on two case studies to demonstrate its superiority as an effective neuro-fuzzy modelling technique.