On-line Extreme Learning Machine for Training Time-Varying Neural Networks

Time-Varying Neural Networks(TV-NN) represent a powerful tool for nonstationary systems identification tasks, as shown in some recent works of the authors. Extreme Learning Machine approach can train TV-NNs efficiently: the reference algorithm is named ELM-TV and is of batch-learning type. In this paper, we generalize an online sequential version of ELM to TV-NN and evaluate its performances in two nonstationary systems identification tasks. The results show that our proposed algorithm produces comparable generalization performances to ELM-TV with certain benefits to those applications with sequential arrival or large number of training data.