17 December 2015 Neural network fusion and inversion model for NDIR sensor measurement
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Proceedings Volume 9816, Optical Fibers and Their Applications 2015; 98160R (2015) https://doi.org/10.1117/12.2220064
Event: 16th Conference on Optical Fibers and Their Applications, 2015, Lublin and Naleczow, Poland
Abstract
This article presents the problem of the impact of environmental disturbances on the determination of information from measurements. As an example, NDIR sensor is studied, which can measure industrial or environmental gases of varying temperature. The issue of changes of influence quantities value appears in many industrial measurements. Developing of appropriate algorithms resistant to conditions changes is key problem. In the resulting mathematical model of inverse problem additional input variables appears. Due to the difficulties in the mathematical description of inverse model neural networks have been applied. They do not require initial assumptions about the structure of the created model. They provide correction of sensor non-linearity as well as correction of influence of interfering quantity. The analyzed issue requires additional measurement of disturbing quantity and its connection with measurement of primary quantity. Combining this information with the use of neural networks belongs to the class of sensor fusion algorithm.
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Sławomir Cięszczyk, Paweł Komada, "Neural network fusion and inversion model for NDIR sensor measurement", Proc. SPIE 9816, Optical Fibers and Their Applications 2015, 98160R (17 December 2015); doi: 10.1117/12.2220064; https://doi.org/10.1117/12.2220064
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