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Performance of deterministic learning in noisy environments
Neurocomputing
An ISS-modular approach for adaptive neural control of pure-feedback systems
Automatica (Journal of IFAC)
Mutual Synchronization of Multiple Robot Manipulators with Unknown Dynamics
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Adaptive neural control for a class of large-scale pure-feedback nonlinear systems
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Information Sciences: an International Journal
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In this paper, identification algorithms whose convergence and rate of convergence hinge on the regressor vector being persistently exciting are discussed. It is then shown that if the regressor vector is constructed out of radial basis function approximants, it will be persistently exciting, provided a kind of "ergodic" condition is satisfied. In addition, bounds on parameters associated with the persistently exciting regressor vector are provided; these parameters are connected with both the convergence and rates of convergence of the algorithms involved.