Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation

Abstract

In measurement systems, environmental conditions are measured based on predefined scenarios. Measured data are then processed in either a decentralized or centralized manner. In advanced systems (especially for distributed data processing), taking artificial intelligence features into consideration could improve measurement performance and reliability. It is assumed as autonomy in measurement system which leads to distributed ldquointelligent data measurement and processingrdquo. In this paper, two different methodologies for ldquotemperature predictionrdquo are compared. A discussion concerning the classification of recorded data is then presented. Both a mathematical approach, the so-called ldquoleast squaresrdquo approach, and a model-free approach, called back-propagation, are applied and compared for temperature approximation. After approximation, the predicted temperature values are compared with real temperature records for classification purposes. The ldquoclassification mechanismrdquo includes signal processing features for improving performance.