Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

Xu Sheng Gan; Can Yang; Hai Long Gao

doi:10.4028/www.scientific.net/AMR.951.274

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 951Optimization Design of Structure and Parameters...

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

Abstract:

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

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Periodical:

Advanced Materials Research (Volume 951)

Pages:

274-277

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.951.274

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Online since:

May 2014

Authors:

Xu Sheng Gan*, Can Yang, Hai Long Gao

Keywords:

Genetic Algorithm (GA), Hierarchical Structure, Neural Network (NN), Radial Basis Function

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* - Corresponding Author

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