A Genetic Designed Beta Basis Function Neural Network for Approximating Multi-Variables Functions

  • Chaouki Aouiti
  • Adel M. Alimi
  • Aref Maalej


We propose in this paper a new genetic algorithm for Beta basis function neural networks (BBFNN). The properties of this ’genetic algorithm are the representation used and the ability to obtain the optimal structure of the BBFNN for approximating a multi-variable function.

Each network is coded as a matrix for which the number of rows is equal to the number of parameters in the function. The genetic algorithm operators change the number of neurons in the hidden layer. Some applications to functions with one and two variables are considered to demonstrate the performance of the BBFNN and of their genetic algorithm based design.


Genetic Algorithm Hide Layer Mutation Operator Crossover Operator Beta Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Chaouki Aouiti
    • 1
  • Adel M. Alimi
    • 2
  • Aref Maalej
    • 1
  1. 1.Department of Mechanical EgineeringLASEM: Laboratory of Electromechanical Systems University of Sfax ENISSfaxTunisia
  2. 2.REGIM: Research Group on Intelligent Machines, Department of Electrical EngineeringUniversity of Sfax, ENISSfaxTunisia

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