Effect of parallel ensembles to self-generating neural networks for chaotic time series prediction
Hiroyuki Inoue, Hiroyuki Narihisa · 2002
Self-generating neural networks (SGNNs) have the features of simplicity of network design and fast processing by automatically constructing a self-generating neural tree (SGNT) from a given training data set. Though the prediction accuracy of SGNNs for chaotic time series prediction is improved by adopting the ensemble averaging method, the computation time increases in proportion to the number of SGNNs in an ensemble. We investigate the improving capability of the prediction accuracy and the parallel efficiency of ensemble SGNNs (ESGNNs) for three chaotic time series prediction problems on a MIMD parallel computer. We allocate each SGNN to each processor. Our results show that the more the number of processors increases, the more the improvement of the prediction accuracy is obtained for all problems.