Neural networks from the perspective of measurement systems

The goal of this paper is to give an overview of neural networks from the viewpoint of measurements. It shows that neural networks can play an important role in measurement systems; for example they can be used in complex sensor´s systems, and in utilization of the observation data, to build system´s models. From the point of view of measurement systems the key property of neural networks is their universal approximation capability. The paper formulates questions about neural networks - which are important for their applications in measurement systems - and gives an overview of the possible answers obtained from the theory of neural networks. The paper contrasts the theoretical results and the engineering questions. It points out that the theoretical results achieved in the last few years are of primary importance; however there are strong limits of the application of the theoretical results.