GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms

  • Authors:

  • A. M. Tang;C. Quek;G. S. Ng

  • Affiliations:

  • Centre for Computational Intelligence, formerly known as the Intelligent Systems Laboratory, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singap ...;Centre for Computational Intelligence, formerly known as the Intelligent Systems Laboratory, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singap ...;Centre for Computational Intelligence, formerly known as the Intelligent Systems Laboratory, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singap ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2005

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Abstract

Fuzzy logic allows mapping of an input space to an output space. The mechanism for doing this is through a set of IF-THEN statements, commonly known as fuzzy rules. In order for a fuzzy rule to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this approach is the difficulty of automatically and accurately constructing the membership functions. Genetic Algorithms (GAs) is a technique that emulates biological evolutionary theories to solve complex optimization problems. Genetic Algorithms provide an alternative to our traditional optimization techniques by using directed random searches to derive a set of optimal solutions in complex landscapes. GAs literally searches towards the two end of the search space in order to determine the optimum solutions. Populations of candidate solutions are evaluated to determine the best solution. In this paper, a hybrid system combining a Fuzzy Inference System and Genetic Algorithms-a Genetic Algorithms based Takagi-Sugeno-Kang Fuzzy Neural Network (GA-TSKfnn) is proposed to tune the parameters in the Takagi-Sugeno-Kang fuzzy neural network. The aim is to reduce unnecessary steps in the parameters sets before they can be fed into the network. Modifications are made to various layers of the network to enhance the performance. The proposed GA-TSKfnn is able to achieve higher classification rate when compared against traditional neuro-fuzzy classifiers.