Abstract
In the case of a large number of applications, especially complex industrial ones, the knowledge on system’s (process, plant, etc.) parameters during the operation of the system is of major importance. However, in real cases, there are always parameters, which are not accessible. In the present work, we focus our interest around the extraction possibility of information relative to inaccessible parameters. Of course, such dilemma becomes a very complex and difficult problem in a general context. However, we will discuss some realistic (and especially, realizable) conditions for which a solution could be approached. In proposed approach, we use the neural network’s learning and a synaptic weight based indicator to detect changes related to system’s inaccessible parameters. Experimental results relative to a real industrial process have been reported validating our approach.
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Madani, K., Berechet, I. (2001). Inaccessible Parameters Monitoring in Industrial Environment: A Neural Based Approach. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_75
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DOI: https://doi.org/10.1007/3-540-45723-2_75
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