Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97611
Title: SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system
Authors: Tung, Sau Wai
Quek, Chai
Guan, Cuntai
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications
Issue Date: 2012
Source: Tung, S. W., Quek, C., & Guan, C. (2012). SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system. Expert systems with applications, 39(17), 12759-12771.
Series/Report no.: Expert systems with applications
Abstract: The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form 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 present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.
URI: https://hdl.handle.net/10356/97611
http://hdl.handle.net/10220/11128
DOI: 10.1016/j.eswa.2012.02.056
Rights: © 2012 Published by Elsevier Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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