An improved genetic algorithm based fuzzy-tuned neural network

S. H. Ling, Hung Fat Frank Leung, H. K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.
Original languageEnglish
Pages (from-to)457-474
Number of pages18
JournalInternational Journal of Neural Systems
Issue number6
Publication statusPublished - 1 Dec 2005


  • Fuzzy logic
  • Genetic algorithm
  • Neural network
  • Neural-fuzzy network

ASJC Scopus subject areas

  • Computer Networks and Communications


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