Informational and algorithmic support of an environmental air monitoring intelligent system based on neural networks

The article discusses algorithmic and informational support of an intelligent control system for modern gas analyzers used in environmental air monitoring systems called the Electronic nose. Neural networks form the base of information support. The paper describes a modern automatic odor recognition system based on measurements using low-selective sensors in multi-sensor systems for detecting components of gas mixtures in ambient air. It also shows the advantage of the proposed system compared with traditional systems with highly selective sensing elements. There is a library of smell images based on a series of prerecorded responses from the sensor matrix. It is stored in the intelligent system database. Then the responses of an analyzed gas are compared with the responses of individual substances from the image library. The authors propose a two-stage data clustering method for information processing. First, observational data is normalized so that each input parameter equally affects the system. Then the data are assembled in-to clusters using self-organizing Kohonen maps and the k-means algorithm. Each cluster represents an odor with a similar smell. Specific assessments are based on experimental data collected in the environmental monitoring system in the area of the waste incineration plant in Kozhukhovo. The paper considers the choice of an odor identification criteria, which will be used by experts in deciding on odor identification. There is a substantiation of choosing the proximity metric of analytical samples as the norm of the distance between the odor vectors in each sample as a criterion. The authors have developed an algorithm for identifying a substance's gas analytical sample using neural networks and the selected criterion for decision-making support. There is also a developed (using R pro-gramming language) software product that allows assessing data membership obtained from a device to a certain smell followed by providing visual results of a odors' spread dynamics in real-time. The paper presents the application results of the developed algorithm in the eco-monitoring system of the incinerator plant in the Kosino-Ukhtomsky district of the Moscow region.