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Mixed Radial Basis Function Neural Network Training Using Genetic Algorithm

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Abstract

The Radial Basis Function Neural Network (RBFNN) is a feedforward artificial neural network employing radial basis functions as activation functions in the hidden layer. The output of the RBFNN is a linear combination of the outputs from the hidden layer. This paper present a Mixed Radial Basis Function Neural Network (MRBFNN) training using Genetic Algorithm (GA). The choice of the type of Radial Basis Functions (RBFs) utilized in each hidden layer neuron has a significant impact on convergence, interpolation and performance. In this work, the authors are introducing a new approach to optimizing the choice of radial basis functions, centers, radius and weights of the output layer. We model in terms of mixed-variable optimization problems with linear constraints. To solve this model we will use an approach based on the genetic algorithm, allows us to determine the types of RBF to use in the hidden layer and the optimal weight of the output layer which gives us a good generalization. The results numerically demonstrate the performance of the theoretic results presented in this paper, as well as the benefits of the new modeling.

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Data Availability

All data used for the experiments are publicly available (as indicated by pointers to the literature in the paper).

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Funding

No funding was received for conducting this study.

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Author notes

  1. Mohammed Ouanan and Hamid Bourray have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Sciences, Faculty of Sciences, Moulay Ismail University, B.P. 11201 Zitoune, 50070, Meknes, Morocco

    Taoufyq Elansari, Mohammed Ouanan & Hamid Bourray

Authors
  1. Taoufyq Elansari
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  2. Mohammed Ouanan
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  3. Hamid Bourray
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Correspondence to Taoufyq Elansari.

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Elansari, T., Ouanan, M. & Bourray, H. Mixed Radial Basis Function Neural Network Training Using Genetic Algorithm. Neural Process Lett 55, 10569–10587 (2023). https://doi.org/10.1007/s11063-023-11339-5

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