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A novel hybrid algorithm for creating self-organizing fuzzy neural networks

Khayat, O ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2009.06.013
  3. Publisher: 2009
  4. Abstract:
  5. A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, GA and PSO are performed to conduct fine tuning for the obtained parameter set of the premise parts and consequent parts in the aforementioned fuzzy model. The proposed algorithm is successfully applied to three tested examples. © 2009 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Fuzzy neural network ; Genetic algorithm ; Fine tuning ; Fuzzy clusters ; Fuzzy models ; Fuzzy structures ; Hybrid algorithms ; Optimal number ; Parameter set ; Precision model ; Self-organizing ; Self-organizing fuzzy neural network ; Takagi-sugeno ; Two phasis ; Xie-Beni index ; Fuzzy clustering ; Genetic algorithms ; Particle swarm optimization (PSO) ; Tuning ; Fuzzy neural networks ; Accuracy ; Artificial neural network ; Cluster analysis ; Computer model ; Controlled study ; Fuzzy system ; Hybrid computer ; Mathematical computing ; Particle size ; Priority journal ; Process optimization ; Statistical parameters ; Validity
  8. Source: Neurocomputing ; Volume 73, Issue 1-3 , 2009 , Pages 517-524 ; 09252312 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0925231209002537