An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis.

Autor:	Azhar Ali, Syed Saad, Moinuddin, Muhammad, Raza, Kamran, Adil, Syed Hasan
Předmět:	ADAPTIVE computing systems MACHINE learning TIME-domain analysis FEEDBACK control systems RADIAL basis functions NEURAL computers PATTERN recognition systems
Zdroj:	Scientific World Journal; 2014, p1-9, 9p
Abstrakt:	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 ?? 2 stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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