An adaptive learning rate for RBFNN using time-domain feedback analysis.
ScientificWorldJournal 2014;
2014:850189. [PMID:
24987745 PMCID:
PMC3980919 DOI:
10.1155/2014/850189]
[Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2013] [Accepted: 01/17/2014] [Indexed: 11/26/2022] Open
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 l2 stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development.
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