Chaotic time series forecasting with QPSO-trained RBF neural network

Radial Basis Function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. In this paper, we use Quantum-behaved Particle Swarm Optimization (QPSO), a newly proposed evolutionary search technique, to train RBF neural network and therefore apply QPSO-trained RBF network in chaotic time series forecasting. The proposed method was test on Mackey-Glass model, and the results show that it can predict the time series more quickly and precisely than the RBF network trained by Particle Swarm Optimization (PSO) algorithm.