Parallel performance of ensemble self-generating neural networks for chaotic time series prediction problems
Hirotaka Inoue, Hiroyuki Narihisa · Systems and Computers in Japan · 2005
Self-Generating Neural Networks (SGNN) have the feature of fast processing by automatically constructing a self-generating neural tree (SGNT) from a given training data set. The prediction accuracy of SGNNs for chaotic time series prediction is improved by ensemble averaging of various SGNNs. However, the computation time increases with the number of SGNNs on a single processor. In this paper, we investigate improvement of the prediction accuracy and parallel efficiency of ensemble SGNNs for chaotic time series prediction problems on an MIMD parallel computer. We allocate each SGNN to one processor. Our results show that as the number of node processors increases, a continuing improvement of prediction accuracy is obtained for all problems, while maintaining the high-speed processing performance of the single SGNN. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(10): 82–92, 2005; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/scj.10297