Genetic Programming, a heuristic optimization technique based on the theory of Genetic Algorithms, is a method successfully used to identify nonlinear model structures by analyzing a system’s measured signals. Mostly, it is used as an offline tool which means that structural analysis is done after collecting all available identification data. In this paper, we propose an enhanced on-line GP approach that is able to adapt its behaviour to new observations while the GP process is executed. Furthermore, an approach using GP for on-line fault diagnosis is described, and finally test results using measurement data of NOx emissions of a BMW diesel engine are discussed.
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ALaRT '05 - International Workshop on Automatic Learning and Real-Time 2005, September 7-8, 2005; Siegen, Germany