Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2932
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKapgate, Sachin N-
dc.contributor.authorGupta, Saurav-
dc.contributor.authorSahoo, Ajit Kumar-
dc.date.accessioned2018-03-08T11:40:03Z-
dc.date.available2018-03-08T11:40:03Z-
dc.date.issued2018-02-
dc.identifier.citation5th International Conference on Signal Processing and Integrated Networks (SPIN 2018),Amity University, Noida, Uttar Pradesh, India, 22 - 23 February, 2018.en_US
dc.identifier.urihttp://hdl.handle.net/2080/2932-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractMany practical systems tend to have some extent of nonlinearity involved in their behavior. System identification and control design for nonlinear dynamical systems is achieving extensive attention in many practical applications. To model nonlinear system behavior, many mathematical models have been developed and employed in practical applications. This paper mainly focuses on application of least mean square (LMS) variants for the adaptive implementation of one such model known as nonlinear Volterra model. The challenging problems involved with the stability and convergence rate of traditional least mean square based approach are shown individually. The supporting analysis and simulations are provided to justify the efficacy of presented work. Least mean square variants based Volterra modeling approaches can be effectively applied in system control design, acoustic echo cancellation and stability analysis of the nonlinear systems.en_US
dc.subjectSignal processingen_US
dc.subjectNonlinear System modelingen_US
dc.subjectVolterra modelen_US
dc.subjectLeast mean squaresen_US
dc.titleAdaptive Volterra Modeling For Nonlinear Systems Based on LMS Variantsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2018_SPIN_SNKapgate_Adaptive.pdfConference Paper663.21 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.