A Novel Kernel-based Extreme Learning Machine with Incremental Hidden Layer Nodes

Abstract

Extreme learning machine (ELM) is widely used in various fields because of its advantages such as short training time and good generalization performance. The input weights and bias of hidden layer of traditional ELM are generated randomly, and the number of hidden layer nodes is determined by artificial experience. Only by adjusting parameters manually can an appropriate network structure be found. This training method is complex and time-consuming, which increases the workload of workers. To solve this problem, the incremental extreme learning machine (I-ELM) is used to determine the appropriate number of hidden layer nodes and construct a compact network structure in this paper. At the same time, a new hidden layer activation function STR is proposed, which avoids the disadvantages of incomplete output information of hidden layer due to uneven distribution of sample data. The proposed algorithm is evaluated by public data sets and applied to the classification of superheat degree (SD) in aluminum electrolysis industry. The experimental results show that STR activation function has a good learning speed, and the proposed algorithm is superior to the existing SD identification algorithm in terms of accuracy and robustness.