Adaptive forgetting factor echo state networks for time series prediction
Zhang Song lin, Xue Li · International Journal of Intelligent Systems Technologies and Applications · 2017
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.