A Gas Recognition Method Based on PCA and PSO-LSSVM

Abstract

Gases in the real environment always exist in the form of mixtures, and effective identification of gas types to reduce the occurrence of safety incidents has become an important direction in the field of gas analysis research. This paper proposes a mixed gas identification method based on PCA and PSO-LSSVM. This method uses principal component analysis (PCA) to reduce the dimensionality of the sensor array output signal, and uses the Relief algorithm to select data. The particle swarm algorithm is used to iteratively optimize the relevant parameters in the least squares support vector machine model, and the PSO-LSSVM model is constructed to qualitatively analyze the composition of the mixed gas. This article uses a public data set of mixed gases of ethylene, methane and carbon monoxide to conduct experiments. The experimental results show that the method used in this paper can effectively identify the type of mixed gas, and the recognition accuracy rate reaches 91.67%. The method proposed in this paper can provide a research foundation for the follow-up analysis of the mixed gas concentration.