Abstract
This paper describes a monitoring and diagnosis process for the detection of evolution of a system whose characteristics vary with time. This is an adaptive fuzzy pattern recognition algorithm that achieves progressive and on-line learning of the system states. A neural net based architecture associated with a real-time algorithm for the detection of abrupt changes provides a diagnosis of evolution that may also be predictive. The algorithm was applied to the monitoring of a car driver's behavior. Results on real data are reported.
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Schedule a callMore From: Intelligent Components and Instruments for Control Applications 1994
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