Evolutionary Neuro-Computing Approaches to System Identification

Jena, Debashisha (2010) Evolutionary Neuro-Computing Approaches to System Identification. PhD thesis.



System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors.

Item Type:Thesis (PhD)
Uncontrolled Keywords:Nonlinear System Identification, Nonlinear Autoregressive Exogenous (NARX), Nonlinear Autoregressive Moving Average Exogenous (NARMAX),Neural Networks (NNs), Back-Propagation (BP), Levenberg-Marquardt (LM), Evolutionary Algorithms (EAs), Evolutionary Computation (EC), Sequential Hybridization (SH)
Subjects:Engineering and Technology > Electrical Engineering > Power Networks
Divisions: Engineering and Technology > Department of Electrical Engineering
ID Code:2774
Deposited By:Hemanta Biswal
Deposited On:28 Jul 2011 09:30
Last Modified:15 Jun 2012 09:47
Supervisor(s):Subudhi, B D

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