THE USE OF ARTIFICIAL NEURAL NETWORKS FOR PROCESSING PRESSURE MEASUREMENT DATA

Abstract

Pressure, in its definition as a physical quantity, is determined by the effect of a force on a plane perpendicular to the direction of this force. Therefore, pressure measurement becomes a necessary component in a wide range of technical and engineering applications. This quantity manifests itself in one way or another in everyday aspects of our lives, but in the context of technical research and innovation, its meaning and importance are revealed to the fullest extent.

Modern pressure measurements play a critical role in various industries, from manufacturing to high-tech scientific research. The importance of this parameter can be explained by the variety of its applications and influence on the functioning of various technical systems.

In today's world, where industry and technology are rapidly advancing, the continuous complexity growth of technological processes gives rise to a significant issue of processing and compensating for measurement data. This issue becomes particularly relevant in high-tech industries, where even small measurement errors can have serious consequences, especially in pressure measurements.

One potential solution to this problem is the utilization of artificial neural networks. This work is aimed at developing a new approach to processing and compensating measurement data, which has the potential to improve the accuracy and efficiency of measurement processes in high-tech industries. The application of artificial neural networks can help reduce errors and ensure more reliable and accurate measurements, which in turn will positively impact measurement accuracy.