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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