The study of using an extreme learning machine for rapid concentration estimation in multi-component gas mixtures

Abstract

Cross-sensitivity is one of the major unpleasant characteristics of metal oxide gas sensors. In order to solve this challenging problem, artificial neural networks have proved to be very powerful tools, among which back propagation (BP) and radial basis function (RBF) neural networks are the two most commonly used ones in data analysis of metal oxide gas sensors and arrays. However, relatively long training time is the major disadvantage for the BP and RBF neural networks. In order to solve this problem, an extreme leaning machine (ELM) is introduced and studied in this paper. Experimental results show that ELM networks can achieve 466 and 333 times faster training speed than the BP and RBF neural networks, respectively. In addition, ELM networks can achieve comparable concentration prediction accuracy to RBF networks which is much better than BP networks.