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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2019

16.01.2019 | Original Article

New sinusoidal basis functions and a neural network approach to solve nonlinear Volterra–Fredholm integral equations

verfasst von: Stefania Tomasiello, Jorge E. Macías-Díaz, Alireza Khastan, Zahra Alijani

Erschienen in: Neural Computing and Applications | Ausgabe 9/2019

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Abstract

In this paper, we present and investigate the analytical properties of a new set of orthogonal basis functions derived from the block-pulse functions. Also, we present a numerical method based on this new class of functions to solve nonlinear Volterra–Fredholm integral equations. In particular, an alternative and efficient method based on the formalism of artificial neural networks is discussed. The efficiency of the mentioned approach is theoretically justified and illustrated through several qualitative and quantitative examples.
Vorheriger Artikel A novel support vector regression algorithm incorporated with prior knowledge and error compensation for small datasets
Nächster Artikel Discrete-time neural network with two classes of bias noises for solving time-variant matrix inversion and application to robot tracking

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Metadaten
Titel
New sinusoidal basis functions and a neural network approach to solve nonlinear Volterra–Fredholm integral equations
verfasst von
Stefania Tomasiello
Jorge E. Macías-Díaz
Alireza Khastan
Zahra Alijani
Publikationsdatum
16.01.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-03984-y

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