A New Algorithm for Identification of Significant Operating Points Using Swarm Intelligence

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.

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