Abstract
In general, to efficiently deal with modeling of complex nonlinear dynamical systems three essential tasks amongst others are involved, namely understanding, predicting, and explaining. The understanding task aims at developing mathematical models of systems relying on available data, measurements and observations in a bottom-up fashion. This allows identifying the system’s dynamics including the external interactions that affect the system’s behavior. Such identification should specify the set of functions (rules or equations) that captures the factors that control the change of the system’s behavior. Often, the identification task is reduced to choosing a certain parameterized model for which an estimation of the unknown parameters is to be performed. Identification as a process may be part of a more general task that is control whose aim is to equip the system with regulation mechanisms that provide stability and the desired performance in presence of feedback and disturbances from the environment. There is no doubt that the identification and control tasks are typical computational tasks that involve various algorithmic paradigms.
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