This document presents a neural network fusion and inversion model to address environmental disturbances impacting measurements from NDIR sensors. The model measures industrial gases with varying temperatures, accounting for changes in influence quantities. Neural networks were applied due to difficulties describing the inverse model mathematically. The neural networks correct sensor non-linearity and interference from disturbing quantities. The issue requires additional measurement of disturbing quantities combined with primary measurements using sensor fusion and neural networks.

1. Neural network fusion and inversion model for NDIR sensor measurement | ISweek
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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. © (2015)
COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading
of the abstract is permitted for personal use only.
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