Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Stable adaptive systems
Universal approximation using radial-basis-function networks
Neural Computation
Persistency of Excitation in Identification Using Radial Basis Function Approximants
SIAM Journal on Control and Optimization
Robust adaptive control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Adaptation and Learning in Automatic Systems
Adaptation and Learning in Automatic Systems
Deterministic Learning Theory for Identification, Recognition, and Control
Deterministic Learning Theory for Identification, Recognition, and Control
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Finite-dimensional sensor orbits and optimal nonlinear filtering
IEEE Transactions on Information Theory
Robust nonlinear system identification using neural-network models
IEEE Transactions on Neural Networks
Using additive noise in back-propagation training
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Deterministic Learning and Rapid Dynamical Pattern Recognition
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper, based on the previous results of deterministic learning, we investigate the performance of deterministic learning in noisy environments. Two different types of noises arising in practical implementations are considered: the system noise and the measurement noise. By employing the convergence results of a class of perturbed linear time-varying (LTV) systems, the effects of these noises upon the learning performance are revealed. It is shown that while there is little effect upon the learning speed, noises have much influence on the learning accuracy. Compared with system noise, the effects of measurement noise appear to be more complicated. Under the noisy environments, robustification technique on the learning algorithm is required to avoid parameter drift. Furthermore, it is shown that additive system noise can be used to enhance the generalization ability of the RBF networks. Simulation studies are included to illustrate the results.