Convergence analysis of adaptive exponential functional link network

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dc.contributor.author Patel, Vinal
dc.contributor.author Bhattacharjee, Sankha Subhra
dc.contributor.author George, Nithin V.
dc.date.accessioned 2020-04-13T10:28:05Z
dc.date.available 2020-04-13T10:28:05Z
dc.date.issued 2021-02
dc.identifier.citation Patel, Vinal; Bhattacharjee, Sankha Subhra and George, Nithin V., “Convergence analysis of adaptive exponential functional link network”, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2020.2979688, vol. 32, no. 2, pp. 882 - 891, Feb. 2021. en_US
dc.identifier.issn 2162-237X
dc.identifier.issn 2162-2388
dc.identifier.uri https://doi.org/10.1109/TNNLS.2020.2979688
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5309
dc.description.abstract The adaptive exponential functional link network (AEFLN) is a recently introduced novel linear-in-the-parameters nonlinear filter and is used in numerous nonlinear applications, including system identification, active noise control, and echo cancellation. The improved modeling accuracy offered by AEFLN for different nonlinear applications can be attributed to the exponentially varying sinusoidal basis functions used for nonlinear expansion. Even though AEFLN has been widely used for the identification of nonlinear systems, no theoretical analysis of AEFLN is available in the literature. Hence, in this article, a theoretical performance analysis of AEFLN trained using an adaptive exponential least mean square (AELMS) algorithm under the Gaussian input assumption is discussed. Expressions describing the mean as well as mean square behavior of the weight vector and adaptive exponential parameter are derived. Computer simulations are carried out, and the derived theoretical expressions show a close correspondence with simulation results.
dc.description.statementofresponsibility by Vinal Patel, Sankha Subhra Bhattacharjee and Nithin V. George
dc.format.extent vol. 32, no. 2, pp. 882 - 891
dc.language.iso en_US en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.subject Excess mean square error en_US
dc.subject Functional link network (FLN) en_US
dc.subject Linear-in-the-parameter nonlinear filter en_US
dc.subject Mean behavior en_US
dc.subject Nonlinear filter en_US
dc.subject Steady-state analysis en_US
dc.subject System identification en_US
dc.title Convergence analysis of adaptive exponential functional link network en_US
dc.type Article en_US
dc.relation.journal IEEE Transactions on Neural Networks and Learning Systems


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