A Nonlinear Manifold Embedding Extreme Learning Machine to Improve the Gas Recognition of Electronic Noses

The drift compensation of gas sensor systems is an important topic in the artificial olfactory community. The drift is arisen by multiple factors, i.e., the change of temperature and moisture, poison effect, manufacture repeatability, and so on. It would arise the bias, distortion, and offset of data and lead it to be unrecognized. Conventionally, the calibration methods in hardware level are laborious and ineffective, and many proposed baseline software methods are not effective enough due to the loss of structure information. In this work, a classifier-level nonlinear manifold transfer extreme learning machine (NMT-ELM) is proposed, which intends to learn the manifold information embedded into the source domain data and accomplish the structure information preservation in a nonlinear style. In addition, the domain adaptation between source and target domain is adopted as well, which is utilized for effective distribution alignment. The output matrix of extreme learning machine (ELM) is determined with supervised learning, and the generalization is enhanced effectively as well. Moreover, a novel intelligent optimization algorithm entitled Cuckoo Searching (CS) is introduced, in order to explore the optimal parameters of NMT-ELM rapidly comparing with the conventional grid searching. The validation is proved by experiments on different open-set electronic nose (E-nose) data, and the classification accuracies comparison demonstrates its effectiveness on drift compensation of E-nose.