Prediction of Chaotic Time Series Based on QPSO:RBF NN
基于QPSO—RBF NN的混沌时间序列预测*

A novel of method of prediction of chaotic time series based on constructing radial basis function neural network using quantum-behaved particle swarm optimization algorithm was proposed.After determination of units of number in RBF layer,all parameters in relevant network such as central position,spreading constant,weights and offsets of RBF NN were coded to particles in learning algorithm.The parameter vector,which has a best adaptation value,was searched globally.The simulation results show the effectiveness of this method.