An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis
Joint Authors
Ali, Syed Saad Azhar
Moinuddin, Muhammad
Raza, Kamran
Adil, Syed Hasan
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Ali, Syed Saad Azhar& Moinuddin, Muhammad& Raza, Kamran& Adil, Syed Hasan. 2014. An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1051334
Modern Language Association (MLA)
Ali, Syed Saad Azhar…[et al.]. An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1051334
American Medical Association (AMA)
Ali, Syed Saad Azhar& Moinuddin, Muhammad& Raza, Kamran& Adil, Syed Hasan. An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1051334
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051334