Parameter monitoring using neural-network-processed chromaticity
A PC-based neural-network processing system is described for the interpretation of chromatic sensor information monitored remotely using a CCD camera. Chromaticity based monitoring provides a means of simple data pre-processing to reduce system noise due to total intensity variation. However, sensing elements using chromaticity usually show complex variations in chromaticity with measurand changes. This contribution shows how these complex variations may be processed to yield high resolution values of a measurand. The paper presents a novel application of neural networks for monitoring temperatures using thermochromic materials and to perform 2-D pressure analysis using photoelastic materials. The inherent complex mapping and generalisation abilities of multi-layered perceptrons (MLP) make them ideal for processing the detected signals. Results are presented showing that the neural network can provide levels of resolution and performance for remotely addressing chromatic transducers, which are acceptable for detailed metrological applications.
