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The paper presents a conception of uncertainty calculation of a result obtained in a direct measurement realized in conditions described by random errors. The conception basis on the error denition being an eect of analysis of a quantization process and, rst of all, it permits to determine uncertainty of a single measurement result in measuring and control systems processing signals varying in time. Division of the errors into two types A and B permits elaboration of such a procedure which enables uncertainty calculation for an average value of a series of measurements in the way close to this one proposed by GUM and widely discussed in last years. Theoretical considerations are illustrated by examples showing practical properties of the presented uncertainty calculation procedures.

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The paper presents a new approach to analysis metrological properties of neural networks usedfor reconstruction of input signal of a nonlinear sensor. The general idea of the reconstruction realization consists in its decomposition to static and dynamic parts properties of which are investigated independently. The analysis of the process ofsignal conversion and reconstruction is made by using the error model containing both propagation of error from input to the output and composition of the propagated errors with the errors introduced by elements realizing the conversion and reconstruction. Theoretical considerations have been illustrated by results obtained from measurement and simulation experiments.Keywordsartificial neural network, signal reconstruction, error model, uncertainty ofa measurement result.1-4244-0589-0/07/$20.00