Type discrimination and concentration prediction towards ethanol using a machine learning–enhanced gas sensor array with different morphology-tuning characteristics
Tao Wang, Hongli Ma, Wenkai Jiang, Hexin Zhang, Min Zeng, Jianhua Yang, Xue Wang, Ke Liu, Renhua Huang, Zhi Yang
Abstract
> 0.98) by the ELM-based regressor, despite the nearly saturated response of the sensor array. This study explores the possibility of pattern recognition analysis based on machine learning to further improve the detection performance of the gas sensor array with different response characteristics regulated by the morphology.
Topics & Concepts
Extreme learning machineSupport vector machinePrincipal component analysisArtificial neural networkBiological systemBackpropagationPattern recognition (psychology)Artificial intelligenceComputer scienceSimulated annealingSolventMaterials scienceMachine learningChemistryOrganic chemistryBiologyAdvanced Chemical Sensor TechnologiesGas Sensing Nanomaterials and SensorsAnalytical Chemistry and Sensors