The paper presents a novel algorithm for identification of significant operating points from non-invasive identification of nonlinear dynamic objects. In the proposed algorithm to identify the unknown parameters of nonlinear dynamic objects in different significant operating points, swarm intelligence supported by a genetic algorithm is used for optimization in continuous domain. Moreover, we propose a new weighted approximation error measure which eliminates the problem of the measurements obtained from non-significant areas. This measure significantly accelerates the process of the parameters identification in comparison with the same algorithm without weights. Performed simulations prove efficiency of the novel algorithm. © 2014 Springer International Publishing.
CITATION STYLE
Dziwiński, P., Bartczuk, Ł., Przybył, A., & Avedyan, E. D. (2014). A new algorithm for identification of significant operating points using swarm intelligence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8468 LNAI, pp. 349–362). Springer Verlag. https://doi.org/10.1007/978-3-319-07176-3_31
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