Self-organizing pipelined recurrent wavelet neural network for time series prediction

Abstract

The pipelined recurrent wavelet neural network (PRWNN) with nested structure has been widely used for time series prediction. However, selecting the appropriate network structure for different problems is still an unsolved problem. To solve this issue, the self-organizing PRWNN (SPRWNN) is proposed. Different from the PRWNN with fixed structure, the number of modules and hidden neurons of SPRWNN can be automatically determined during the training process. Firstly, the number of hidden neurons in each module of the SPRWNN is adjusted by the spiking strength (SS) of nodes. Then, the module growth mechanism is designed by measuring the performance of the SPRWNN. Furthermore, the convergence of SPRWNN is proved theoretically. The experimental results show that the SPRWNN can not only automatically adjust the network structure, but also improves the prediction accuracy by about 40% as compared with the PRWNN with fixed structure.