MULTI-MODELS IDENTIFICATION METHODS COMPARISON IN THE NON-LINEAR DYNAMIC SYSTEM IDENTIFICATION TASK

A. I. Guda, A. I. Mikhalyov

Abstract


In this article a couple of identification methods for non-linear (possibly chaotic) dynamic systems are under consideration. Advantages
and drawbacks of existent methods are mentioned. All methods under consideration make use a number of models. Different tactics for the
models parameter movement for identification task solving are proposed. The simplest tactic uses models with fixed parameters. This
method have simple realization, provide best identification speed and worst accuracy. Method with band-limited models allows us achieve
better accuracy due to each model moving to its local extremum, but suffers to high-frequency oscillation, due to ignorance of the
identification system dynamic itself. Approach with models, which movement simulates body movement under external forces and viscous
friction demonstrates minimal identification errors among with significant speed. Identification process simulations are conducted and
conclusion are made. According to simulation results advantages are highlighted and drawbacks are studied. Conclusions allows to make
correct choice in identification method selection task. Also the results allows us to correctly chose some parameters on the identification
system.

Keywords


non-linear dynamic system identification, multi-model identification methods, models ensemble, simulation, extemum estimation.

References


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DOI: https://doi.org/10.15588/1607-3274-2016-4-14



Copyright (c) 2017 A. I. Guda, A. I. Mikhalyov

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