Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

Xu Sheng Gan; Xue Qin Tang; Hai Long Gao

doi:10.4028/www.scientific.net/AMM.713-715.1855

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715Research on Nonlinear Approximation Model of...

Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

Abstract:

In order to improve the modeling efficiency of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

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Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

1855-1858

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.1855

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Online since:

January 2015

Authors:

Xu Sheng Gan*, Xue Qin Tang, Hai Long Gao

Keywords:

Artificial Fish Swarm Algorithm, Neural Network, Nonlinear Function, Radial Basis Function

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* - Corresponding Author

References

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