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Design of Sign Fractional Optimization Paradigms for Parameter Estimation of Nonlinear Hammerstein Systems

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dc.contributor.author Chaudhary, N. I.
dc.contributor.author Aslam, M. S.
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Raja, M. A. Z.
dc.date.accessioned 2020-05-20T19:13:11Z
dc.date.available 2020-05-20T19:13:11Z
dc.date.issued 2019
dc.identifier.citation Chaudhary, N.I...et al. (2019). "Design of Sign Fractional Optimization Paradigms for Parameter Estimation of Nonlinear Hammerstein Systems", Neural Computing and Applications. tr_TR
dc.identifier.issn 09410643
dc.identifier.uri http://hdl.handle.net/20.500.12416/3968
dc.description.abstract Fractional calculus plays a fundamental role in understanding the physics of nonlinear systems due to its heritage of uncertainty, nonlocality and complexity. In this study, novel sign fractional least mean square (F-LMS) algorithms are designed for ease in hardware implementation by applying sign function to input data and estimation error corresponding to first and fractional-order derivative terms in weight update mechanism of the standard F-LMS method. Theoretical expressions are derived for proposed sign F-LMS and its variants; strength of methods for different fractional orders is evaluated numerically through computer simulations for parameter estimation problem based on nonlinear Hammerstein system for low and high signal–noise variations. Comparison of the results from true parameters of the model illustrates the worth of the scheme in terms of accuracy, convergence and robustness. The stability and viability of design methodologies are examined through statistical observations on sufficiently large number of independent runs through mean square deviation and Nash–Sutcliffe efficiency performance indices. tr_TR
dc.language.iso eng tr_TR
dc.publisher Springer London LTD tr_TR
dc.relation.isversionof 10.1007/s00521-019-04328-0 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Fractional Adaptive Signal Processing tr_TR
dc.subject Control Structures tr_TR
dc.subject Nonlinear System Identification tr_TR
dc.subject Hammerstein Models tr_TR
dc.subject Sign Regressors tr_TR
dc.title Design of Sign Fractional Optimization Paradigms for Parameter Estimation of Nonlinear Hammerstein Systems tr_TR
dc.type article tr_TR
dc.relation.journal Neural Computing and Applications tr_TR
dc.contributor.authorID 56389 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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