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This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface control technique is incorporated into radial-basis-function neural networks (NNs)-based control framework to eliminate the problem of explosion of complexity. To avoid the analytic computation, the command filter is employed to produce the command signals...
This work describes a new approach of the adaptive retraining model for data forecasting. This time, six predictors are simultaneously employed in order to produce a better forecasting for electric load. By doing so, the new forecasting system eliminates iterative simulation. The set of predictors is regularly trained in order to be adjusted to the latest modifications of the input data. The new approach...
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