Abstract
Drawbacks of the adaptive-searching methods, related with the problem of multi-parameter dynamic system identification are explored and highlighted. New approach, based on “moving regression” method is proposed. New approach is a hybrid method; it combines features of the “moving average” method, linear regression method and differential system representation. This combination allows to simultaneously determining complex dynamic system parameters, in spite of its chaotic behavior and measurement errors. New method possibilities are explored via identification process numerical simulation for the Lorenz chaotic system.
Highlights
Thereby, creation of the methods and algorithms, suitable for the identification task solving in the case of the several parameters is an actual problem
«Системні технології» 6 (125) 2019 «System technologies» In spite of successive identification of every parameter, there is no direct expand to the case of non-single parameter
Using the results of the simulation, we can derive following conclusions: 1. Direct utilization of the criteria based approach in most cases can not lead to successful identification in the presence of more the one parameters
Summary
Non-empty set of the identification methods, suitable for complex and chaotic systems identification, exist and used with some order of success. Existence of the single scalar criteria does not allow us to conduct identification, if the number of the parameters is more the one. Thereby, creation of the methods and algorithms, suitable for the identification task solving in the case of the several parameters is an actual problem. An adaptive-search method with ensemble of the agents allows as to reach identification goal for every single parameter [3,6].
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