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

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C. GuanAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 39, Issue 17

Pages 12759 - 12771

https://doi.org/10.1016/j.eswa.2012.02.056

Published: 01 December 2012 Publication History

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.

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Cited By

Saad Saoud LGhorbani RRahmoune F(2017)Cognitive Quaternion Valued Neural Network and some applicationsNeurocomputing10.1016/j.neucom.2016.09.060221:C(85-93)Online publication date: 19-Jan-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.060
Saad Saoud LRahmoune FTourtchine VBaddari K(2014)Generalized dynamical fuzzy model for identification and predictionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.5555/2595223.259523926:4(1771-1785)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.5555/2595223.2595239
D'Andrea ELazzerini B(2013)A hierarchical approach to multi-class fuzzy classifiersExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.12.09740:9(3828-3840)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.eswa.2012.12.097

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 39, Issue 17

December, 2012

349 pages

ISSN:0957-4174

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Copyright © © 2012.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 December 2012

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Cited By

Saad Saoud LGhorbani RRahmoune F(2017)Cognitive Quaternion Valued Neural Network and some applicationsNeurocomputing10.1016/j.neucom.2016.09.060221:C(85-93)Online publication date: 19-Jan-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.060
Saad Saoud LRahmoune FTourtchine VBaddari K(2014)Generalized dynamical fuzzy model for identification and predictionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.5555/2595223.259523926:4(1771-1785)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.5555/2595223.2595239
D'Andrea ELazzerini B(2013)A hierarchical approach to multi-class fuzzy classifiersExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.12.09740:9(3828-3840)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.eswa.2012.12.097

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