Designing an optimized linear adaptive filtering algorithm

dc.accession.numberT00704
dc.classification.ddc005.1 TRI
dc.contributor.advisorDas, Rajib Lochan
dc.contributor.authorTrivedi, Vishwas
dc.date.accessioned2019-03-19T09:30:51Z
dc.date.accessioned2025-06-28T10:19:50Z
dc.date.available2019-03-19T09:30:51Z
dc.date.issued2018
dc.degreeM. Tech
dc.description.abstractFor any system, sparse or dispersive, z2 proportionate algorithm of Proportionate Normalized Least Mean Square (PtNLMS) class, gives the best convergence rate compare to any other weiner filter based iterative algorithms. Though, to implement this algorithm practically, is impossible due to lack of information about the unknown system weight vector wopt, which has been directly used in the formation of the gain proportionality function in z2 proportionate algorithm. For that to formulate, an approximate adaptive model for proportionate gain function is developed using the fuzzy logic. Then using this new proposed approach, formulated the proportionality gain function for some standard adaptive algorithms like NLMS, PNLMS and their convex combinations. Later on, another algorithm of PtNLMS class is developed by formulating the deterministic adaptive gain function, which gives huge improvement in terms of the convergence rate of adaptive filter and provides better stability compare to some adavanced algorithm like IPNLMS.
dc.identifier.citationTrivedi, Vishwas (2018). Designing an Optimized Linear Adaptive Filtering Algorithm. Dhirubhai Ambani Institute of Information and Communication Technology, vii, 29 p. (Acc. No: T00704)
dc.identifier.urihttp://drsr.daiict.ac.in/handle/123456789/738
dc.publisherDhirubhai Ambani Institute of Information and Communication Technology
dc.student.id201611010
dc.subjectAlgorithms
dc.subjectFuzzy logic
dc.titleDesigning an optimized linear adaptive filtering algorithm
dc.typeDissertation

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