Abstract
The utilisation of formal artificial intelligence (AI) tools has been implemented to produce a hybrid system for optical emission spectral analysis that combines a multilayer perceptron neural network with rule-based system techniques. Even though optical emission spectroscopy is extensively used as an in-situ diagnostic for ionised gas plasmas in manufacturing processes, ways of interpreting the spectra without prior knowledge or expertise from the user's stand-point has encouraged the use of Al techniques to automate the interpretation process. The hybrid approach presented here combines a modified network architecture with a simple rule-base in order to produce explicit models of the identifiable chemical species.
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© 1998 Springer-Verlag
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Ampratwum, C.S., Picton, P.D., Hopgood, A.A., Browne, A. (1998). Hybridization techniques in optical emission spectral analysis. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_765
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DOI: https://doi.org/10.1007/3-540-64582-9_765
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