Adaptive forgetting factor echo state networks for time series prediction Online publication date: Wed, 04-Jan-2017
by Zhang Song-lin; Li Xue
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 16, No. 1, 2017
Abstract: Echo state networks (ESN) are an emerging learning technique proposed for generalised single-hidden layer feed forward networks (SLFNs). However, the conventional ESN ignores training data timeliness, which may reduce prediction accuracy for time varying data. To solve this problem, a novel algorithm based on ESN with adaptive forgetting factor (AF-ESN) is proposed. The adaptive forgetting factor is introduced to ESN sequential learning phase, which automatically tunes the valid training data window size according to prediction error magnitude. A comparison of the proposed AF-ESN with other algorithms is evaluated on three chaotic time series and an actual time series. Compared with conventional ESN and FOS-ELM (online sequential extreme learning machine with forgetting mechanism), though AF-ESN consumes much computation time, AF-ESN provides the highest prediction accuracy with high stability.
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