An online sequential multiple hidden layers extreme learning machine method with forgetting mechanism

Abstract

In many practical applications, training data are presented one-by-one or chuck-by-chuck and also have the property of timeliness very frequently. The ensemble of an online sequential extreme learning machine (EOS-ELM) can learn data one-by-one or chunk-by-chunk with fixed or varying chunk size. The online sequential extreme learning machine with forgetting mechanism (FOS-ELM) can learn data with the property of timeliness. In many practical applications, such as stock forecasting or weather forecasting, the training accuracy can be improved by discarding the outdated data and reducing the influence on later training processes. Since the real-time variations of data are accompanied by a series of unavoidable noise signals, to make the training output closer to the actual output, an online sequential multiple hidden layers extreme learning machine with forgetting mechanism (FOS-MELM) is proposed in this paper. The proposed FOS-MELM can retain the advantages of FOS-ELM, eliminate the influence of unavoidable noise and improve the prediction accuracy. In this work, experiments have been completed on chemical (styrene) data. The experimental results show that FOS-MELM has high accuracy, better stability and better short-term prediction than FOS-ELM.