Study on RBF NN based on improved differential evolution

Authors:
He Dakuo;Wang Fuli;Jia Mingxing
Affiliations:
Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang;Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang;Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 3
Cited 0

Universal approximation using radial-basis-function networks

Neural Computation
Nonlinear one-step-ahead control using neural networks: control strategy and stability design

Automatica (Journal of IFAC)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel method of nonlinear system modeling using radial basis function neural network based on improved differential evolution algorithm is proposed. Differential evolution algorithm is presented to in order to improve modeling capability. Local operator and optimization selection strategy is presented to improve the searching speed and the local searching capability of genetic algorithm. According to the characteristics of radial basis function neural network and differential evolution algorithm, radial basis function neural network and differential evolution algorithm are associated to improve modeling precision. The simulation results show the effectiveness of this method.