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.
|Title of host publication
|Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
|1 PART 1
|Published - 2008
|IFAC Proceedings Volumes (IFAC-PapersOnline)
|1 PART 1
Bibliographical noteFunding Information:
★ This work is sponsored by King Fahd University of Petroleum & Minerals and SABIC under project SABIC 2006-11
The authors acknowledge the support of King Fahd University of Petroleum & Minerals and SABIC for funding this work under project SABIC 2006-11.
- Closed loop identification
- Identification for control
- Nonlinear system identification
ASJC Scopus subject areas
- Control and Systems Engineering