Application Research on Gas Detection with Artificial Olfactory System

By study and simulation on the theoretic model and principle of electronic nose system in artificial olfactory system, this paper proposes an electronic nose system which combines gas sensor array and artificial neural network recognition algorithm. In order to testify systematic rationality and experimental realizability which is implemented in multiple gas measurement, we discuss related methods and steps which apply the system to quality and quantity of multiple gas. Through the analysis on experiments of gas detection which combines gas sensor array and pattern recognition technology, we apply BP feed forward neural network to analyze three gases in quality and quantity. Our research involves experimental configuration method of low concentration gas, input, output and hidden layer element determination method of BP feed forward network in quality and quantity analysis of multiple gases, signal pretreatment algorithm and gas sensor array response vector normalization. The experimental data show that the system designed in this paper makes recognition of 3 gases with 100% qualitative recognition and lower error of quantitative recognition.