TY - GEN
T1 - Feedback analysis of radial basis functions neural networks via small gain theorem
AU - Ali, S. Saad Azhar
AU - Shafiq, Muhammad
AU - Ba-Khashwain, Jamil M.
AU - Al-Sunni, Fouad M.
N1 - Funding Information:
★ This work is sponsored by King Fahd University of Petroleum & Minerals and SABIC under project SABIC 2006-11
Funding Information:
The authors acknowledge the support of King Fahd University of Petroleum & Minerals and SABIC for funding this work under project SABIC 2006-11.
PY - 2008
Y1 - 2008
N2 - Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control, time series prediction, etc. In this paper, feedback analysis of the learning algorithm of radial basis function neural networks is presented. It studies the robustness of the learning algorithm in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. The learning scheme is first associated with a feedback structure and then the stability of that feedback structure is analyzed via small gain theorem. The analysis suggests bounds on the learning rate in order to guarantee that the learning algorithm will behave as robust nonlinear filters and optimal choices for faster convergence speeds.
AB - Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control, time series prediction, etc. In this paper, feedback analysis of the learning algorithm of radial basis function neural networks is presented. It studies the robustness of the learning algorithm in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. The learning scheme is first associated with a feedback structure and then the stability of that feedback structure is analyzed via small gain theorem. The analysis suggests bounds on the learning rate in order to guarantee that the learning algorithm will behave as robust nonlinear filters and optimal choices for faster convergence speeds.
KW - Closed loop identification
KW - Identification for control
KW - Nonlinear system identification
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U2 - 10.3182/20080706-5-KR-1001.1686
DO - 10.3182/20080706-5-KR-1001.1686
M3 - Conference contribution
AN - SCOPUS:79961018079
SN - 9783902661005
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
BT - Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
T2 - 17th World Congress, International Federation of Automatic Control, IFAC
Y2 - 6 July 2008 through 11 July 2008
ER -