SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

  • Authors:

  • S. W. Tung;C. Quek;C. Guan

  • Affiliations:

  • Center for Computational Intelligence, Block N4 #2A-32, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Center for Computational Intelligence, Block N4 #2A-32, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Center for Computational Intelligence, Block N4 #2A-32, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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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.