• DocumentCode
    710487
  • Title

    A hybrid of cuckoo search and simplex method for fuzzy neural network training

  • Author

    Jyh-Yeong Chang ; Shih-Hui Liao ; Shang-Lin Wu ; Chin-Teng Lin

  • Author_Institution
    Dept. of Electr. & Control Eng. Nat., Chiao-Tung Univ., Hsinchu, Taiwan
  • fYear
    2015
  • fDate
    9-11 April 2015
  • Firstpage
    13
  • Lastpage
    16
  • Abstract
    In this paper, a new hybrid algorithm mixing the simplex method of Nelder and Mead (NM) and the cuckoo search (CS), abbreviated as NM-CS, is proposed for the training of the Fuzzy Neural Networks (FNNs). In standard CS, cuckoo birds engage the obligate brood parasitism by laying their own eggs to other host birds. If a host bird discovers the alien eggs, they will either throw these eggs away or abandon its nest and build a new nest elsewhere. In the proposed hybrid algorithm, instead of using the probability to discover an alien egg for the CS, we use the concept of a simplex which is used in the NM algorithm to abandon and generate the new nests. Our proposed method puts more emphasis on exploration of the search space and enhances the ability to avoid local optimum. Some simulation problems will be provided to compare the performances of the proposed method and other methods in training an FNN. In these simulations, it is observed that the proposed method outperforms other methods.
  • Keywords
    fuzzy neural nets; learning (artificial intelligence); search problems; NM-CS; Nelder and Mead; alien eggs; brood parasitism; cuckoo search; fuzzy neural network training; host bird; search space; simplex method; Approximation algorithms; Approximation methods; Birds; Fuzzy neural networks; Testing; Training; Cuckoo Search (CS); Fuzzy Neural Network (FNN); simplex method of Nelder and Mead (NM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on
  • Conference_Location
    Taipei
  • Type

    conf

  • DOI
    10.1109/ICNSC.2015.7116002
  • Filename
    7116002