title: An adaptive learning rate for RBFNN using time-domain feedback analysis.
abstract: 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.
authors.affiliation: Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia.
authors.forename: Syed Saad Azhar
authors.initials: SS
authors.lastname: Ali
authors.affiliation: Faculty of Engineering, Sciences and Technology, Iqra University, Defence View, Karachi 75500, Pakistan.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Moinuddin
authors.affiliation: Faculty of Engineering, Sciences and Technology, Iqra University, Defence View, Karachi 75500, Pakistan.
authors.forename: Kamran
authors.initials: K
authors.lastname: Raza
authors.affiliation: Faculty of Engineering, Sciences and Technology, Iqra University, Defence View, Karachi 75500, Pakistan.
authors.forename: Syed Hasan
authors.identifier: 0000-0003-1280-6645
authors.initials: SH
authors.lastname: Adil
mesh_terms: D000465:Algorithms; D005246:Feedback; D016571:Neural Networks, Computer