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.
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