A Synaptic Indicator Based Approach For Hidden Parameters Extraction In Industrial Environment
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, which is a difficult problem in a general context. 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.
KeywordsArtificial Neural Network Output Neuron Synaptic Weight Internal Parameter Virtual Sensor
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