关键词: 人工神经网络, 自适应集成, 选择性集成, 在线极限学习机

Abstract:

Since the single online sequential extreme learning machine's performance is unstable, it propose an adaptive and selective OSELM. Firstly，it initializes the multiple online sequential extreme learning machine model, then adjustes adaptively the integrated weight of every online sequential extreme learning machine according to their training error and variance for each batch of data, and deletes the model that its integrated weight is smaller than the threshold to improve the training speed dynamically. Finally, the high accuracy and good generalization's model will be selected for integrated prediction. Experimental results show that the ASE-OSELM has higher forecast accuracy and generalization ability than BPNN、LS-SVM and OSELM.

Key words: artificial neural network, adaptive ensemble, selective ensemble, OSELM