• DocumentCode
    518596
  • Title

    Design for self-organizing fuzzy neural networks based on adaptive evolutionary programming

  • Author

    Liu Fang

  • Author_Institution
    Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
  • Volume
    3
  • fYear
    2010
  • fDate
    27-29 March 2010
  • Firstpage
    251
  • Lastpage
    254
  • Abstract
    A novel hybrid learning algorithm based on a evolutionary programming to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on evolutionary programming, to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. construct and parameters of the fuzzy neural network is trained by evolutionary algorithms. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive comparisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.
  • Keywords
    adaptive systems; evolutionary computation; fuzzy neural nets; learning (artificial intelligence); self-adjusting systems; Takagi-Sugeno type fuzzy model; adaptive evolutionary programming; hybrid learning algorithm; self-organizing fuzzy neural network; Adaptive systems; Algorithm design and analysis; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic programming; Neural networks; Neurons; Partitioning algorithms; Takagi-Sugeno model; Fuzzy Neural Networks; evolutionary programming; fuzzy rule;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Control (ICACC), 2010 2nd International Conference on
  • Conference_Location
    Shenyang
  • Print_ISBN
    978-1-4244-5845-5
  • Type

    conf

  • DOI
    10.1109/ICACC.2010.5486626
  • Filename
    5486626