Title: Research on modelling and optimisation of RBF neural network based on particle filter

Authors: Yongwei Li, Jing Li, Jia Zhong

Addresses: College of Electrical Engineering and Information Science, Hebei University of Science and Technology, Shijiazhuang Hebei, 050018, China. ' College of Electrical Engineering and Information Science, Hebei University of Science and Technology, Shijiazhuang Hebei, 050018, China. ' College of Electrical Engineering and Information Science, Hebei University of Science and Technology, Shijiazhuang Hebei, 050018, China

Abstract: The BP neural network algorithm used in approximation of function has two shortcomings: the one is its slower convergence rate and the other is its falling into the local minimum. In order to solve the problems, a new method is proposed in this paper and is complicated on a typical complex system – the synthetic ammonia decarbonisation industrial process. The main issue of the proposed approach is on modelling and optimisation of the radial basis function (RBF) neural network based on particle filter algorithm. The learning capability and the advantage of particle filtering algorithm on processing non-linear system are used and the modelling and optimisation of RBF neural network based on particle filter is present. Furthermore, some simulation studies with calcination section have been done. The simulation result shows the superiority of the RBF neural network based on particle filter algorithm. It provides an efficient way for the complex system modelling and optimisation control research. Both the experimental results and the application validate the feasibility of the proposed algorithm.

Keywords: RBF neural networks; particle filters; modelling; optimisation; synthetic ammonia decarbonisation; co-alkalis; function approximation; convergence rate; local minimum; particle filtering; nonlinear systems; simulation; calcination.

DOI: 10.1504/IJMIC.2010.037033

International Journal of Modelling, Identification and Control, 2010 Vol.11 No.3/4, pp.218 - 223

Available online: 21 Nov 2010 *

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