In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables. It is shown that such a real-time correction can be carried out using simple linear perceptrons. Due to the use of a neural technique, knowledge of the dynamic parameters of the transducer is not necessary. Theoretical considerations are illustrated by the results of simulation studies performed for the modeled second order transducer. The most important properties of the neural dynamic error correction, when emphasizing the fundamental advantages and disadvantages, are discussed.

A novel method of dielectric loss factor measuring has been described. It is based on a quasi-balanced method for the capacitance measurement. These AC circuits allow to measure only one component of the impedance. However, after analyzing a quasi-balanced circuit's processing equation, it is possible to derive a novel method of dielectric loss factor measuring. Dielectric loss factor can be calculated after detuning the circuit from its quasi-equilibrium state. There are two possible ways of measuring the dielectric loss factor. In the first, the quasi-balancing of the circuit is necessary. However, it is possible to measure capacitance of an object under test. In the second method, the capacitance cannot be measured. Use of an artificial neural network minimizes errors of the loss factor determining. Simulations showed that the appropriate choice of the range of the detuning can minimize errors as well.

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The use of artificial neural networks in the field of measurement is mainly because of the need to meet ever increasing demands on the speed of obtaining the measurement results and increase their accuracy. A neural network, which is one of the components of a measurement signal processing chain, represents a specific type of a transducer, and therefore there is a need to determine the uncertainty of the results obtained by the network. Determination of this uncertainty is necessary for the purpose of comparing the metrological properties of neural networks of different structures, learned using different algorithms, as well as for comparing the classical and neural data processing methods that allow obtaining sufficiently accurate measurement results. This study presents a method for uncertainty estimation which is based on the histogram of neural network output result errors obtained in the testing process. The method allows determining the width of the uncertainty interval of final results for a given level of confidence. The theoretical considerations are complemented by simulation results of the measuring chain which uses neural networks for reconstruction of the input signal of a non-linear sensor.