Abstract
Two design prototypes of neural network for on-line process chnage detection in dynamic model-switching environments are presented. The process model is assumed to follow either an ARMAX or a non-linear Volterra representation. The connection strengths of the first neural network are adaptable parameter estimates of the underlying process model. The second prototype is based on the learning vector quantization procedure. Two important design considerations are discussed; i.e., what type of distance measures are suitable for process change detection and how the neural networks should be trained for on-line applications. Various change detection measures and training procedures are also discussed. Finally, on-line performance of the proposed neural networks is demonstrated via computer simulation experiments.
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© 1992 Springer-Verlag Berlin · Heidelberg
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Sastri, T. (1992). Neural Networks for Detection of Process Change in Manufacturing Systems. In: Fandel, G., Gulledge, T., Jones, A. (eds) New Directions for Operations Research in Manufacturing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77537-6_22
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DOI: https://doi.org/10.1007/978-3-642-77537-6_22
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