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论文类型：期刊论文

发表时间：2012-04-01

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：82

页面范围：186-195

ISSN号：0925-2312

关键字：Echo state network; Dual estimation; Kalman filter; Time series; Prediction

摘要：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.