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We have developed an artificial neural network to determine the components of error in measuring the angles by automated goniometric systems whose change over time is a non-stationary random process. There are known techniques for processing measurement results and normalizing the systematic and random components of measurement errors, they have been applied for many years, they are well justified, maximally formalized, fundamentally different and are governed by respective regulations. However, it is still a rather difficult and labor-intensive procedure to determine exactly which component of an error is present in the measurement results. A given procedure is based on using the Fisher’s dispersion criterion. In order to automate this procedure and improve performance efficiency of performed operations, we have developed an artificial neural network (ANN) and examined its functioning. It was determined that the proposed ANN could be successfully employed instead of known analytical-computational procedure using the Fisher’s dispersion criterion. The application of ANN could significantly reduce labor intensity and improve the efficiency of determining the systematic and random components of measurement errors. This is predetermined by the capability of ANN to perform parallel processing of measurement data in real time. The practical implementation of ANN is based on using the neuro-simulator Neural Analyzer, analytical software Deductor Professional developed by BaseGroupLabs. We trained ANN and tested its functionality on the set of simulation results and actual multiple observations when measuring the plane angle of a 24-facet prism. The ability of ANN to quickly and correctly determine components of measurement errors at the stage of analysis of measurement information makes it possible to subsequently define methods for its further processing in accordance with regulatory requirements. That would improve the accuracy and reliability of measurement results as it could help avoid incorrect and inaccurate calculations when normalizing measurement errors.

Development of artificial neural network for determining the components of errors when measuring angles using a goniometric software-hardware complex