Determining the characteristics of water pollutants by neural sensors and pattern recognition methods.
In this paper we have researched the influence of pollutants on such biological objects as photosynthesizing systems in order to reveal the capabilities and features of their application as the controlling sensor in integral ecological monitoring microsystems. It is proposed to elaborate upon the intelligent sensor on the basis of: (1) neural network technologies; (2) the possibility to separate the characteristics of the substances dissolved in water by means of the methods which recognize patterns in a functional space of the fluorescence curves; (3) the results of the chromatographic analysis of standard water samples. This sensor allows to predict water state and to make the optimal decisions for correcting an ecosystem's condition. The efficiency of such a system for water analysis can be improved using the dual measurement principle. This principle suggests identification of a biosensor model according to experimental data.
Yatsenko
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Department of Control Systems, Institute of Cybernetics, Kiev, Ukraine.
D001419:Bacteria / Q000378:metabolism; D015374:Biosensing Techniques; D056890:Eukaryota / Q000378:metabolism; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010788:Photosynthesis / Q000187:drug effects; D012242:Rhodobacter sphaeroides / Q000378:metabolism; D013050:Spectrometry, Fluorescence; D014873:Water Pollutants / Q000032:analysis* / Q000633:toxicity
D014873:Water Pollutants