Release Time:2019-03-09  Hits:

Indexed by: Journal Article

Date of Publication: 2012-04-01

Journal: NEUROCOMPUTING

Included Journals: Scopus、EI、SCIE

Volume: 82

Page Number: 186-195

ISSN: 0925-2312

Key Words: Echo state network; Dual estimation; Kalman filter; Time series; Prediction

Abstract: When using echo state networks (ESNs) to establish a regression model for noisy nonlinear time series, only the output uncertainty was usually concerned in some literature. However, the unconsidered internal states uncertainty is actually important as well. In this study, an improved ESN model with noise addition is proposed, in which the additive noises describe the internal state uncertainty and the output uncertainty. In terms of the parameters determination of this prediction model, a nonlinear/linear dual estimation consisting of a nonlinear Kalman filter and a linear one is proposed to perform the supervised learning. For verifying the effectiveness of the proposed method, the noisy Mackey Glass time series and the generation flow of blast furnace gas (BFG) in steel industry practice are both employed. The experimental results demonstrate that the proposed method is effective and robust for noisy nonlinear time series prediction. (c) 2011 Elsevier B.V. All rights reserved.