Predicting chaotic time series by ensemble self-generating neural networks
Hiroyuki Inoue, Hiroyuki Narihisa · 2000
We introduce ensemble self-generating neural networks (ESGNNs) for chaotic time series prediction. ESGNNs combine the ensemble averaging method with SGNNs. ESGNNs create self-generating neural trees (SGNTs) to shuffle the order of given training data independently, and the network output is averaged of all SGNT outputs. We investigate the improving capability of ESGNNs for three chaotic time series, and compare them with the backpropagation neural networks. Experimental results show that using various SGNTs through the ensemble averaging method significantly improves the predictive performance of ESGNNs on diverse chaotic time series.