Error model application in neural reconstruction of nonlinear sensor input signal

This paper presents a new approach to the analysis of the metrological properties of a measuring chain with neural networks used for the reconstruction of an input signal of a nonlinear sensor. The general idea of this approach consists of building such an error model, which contains both the error sources arising in all the elements of the measuring chain with neural reconstruction and a description of the error propagation from the input to the output of the chain. The error source extraction requires the decomposition of the static and dynamic properties of the sensor, which results in the realization of the reconstruction in two stages independently. This paper shows how to use a built-up error model, which is described in probabilistic categories, for the analysis of the metrological properties of the measuring chain and to calculate uncertainty at every stage of the analysis. Theoretical considerations have been illustrated by the results obtained from measurement and simulation experiments.