relation: https://khub.utp.edu.my/scholars/5438/
title: An adaptive learning rate for RBFNN using time-domain feedback analysis
creator: Ali, S.S.A.
creator: Moinuddin, M.
creator: Raza, K.
creator: Adil, S.H.
description: Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to the l 2 stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development. © 2014 Syed Saad Azhar Ali et al.
publisher: ScientificWorld Ltd.
date: 2014
type: Article
type: PeerReviewed
identifier:   Ali, S.S.A. and Moinuddin, M. and Raza, K. and Adil, S.H.  (2014) An adaptive learning rate for RBFNN using time-domain feedback analysis.  The Scientific World Journal, 2014.   ISSN 1537744X     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897543058&doi=10.1155%2f2014%2f850189&partnerID=40&md5=80887e8df04e77b3bfbc35f665dd9de8
relation: 10.1155/2014/850189
identifier: 10.1155/2014/850189