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Then, radial basis function (RBF) neural networks (NNs) and minimal learning parameter approach are employed to approximate unknown nonlinearities.
Simulation result shows that the controller could achieve good tracking performance with minimal learning parameter in case of actuator fault.
On the other hand, whenever the connected move to smartphones, their mental model of QWERTY keyboards helps them to cope, resulting in a minimal learning curve.
A minimal learning curve, concise coding, and powerful language paradigm have made Ruby an attractive option for coding bioinformatics tools, such as BioRuby [17].
Furthermore, the minimal learning parameter strategy via norm estimation technique is introduced to reduce the multi-parameter adaptive computation burden of fuzzy approximation of the lump uncertainties.
With the help of the MLP technique, the problem of "curse of dimension" is circumvented, and the adaptive mechanism with minimal learning parameterization is achieved.
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Furthermore, a minimal-learning parameter (MLP) approach is employed to update the norm rather than the elements of RBFNNs' weight vectors, which yields a low computational load design.
The reinforcement learning control with neural networks (NNs) is investigated for a class of pure-feedback systems in discrete time using minimal-learning-parameter (MLP) technique.
Neural networks (NNs) are employed to approximate the unknown vehicle dynamics and a minimal-learning parameter scheme is utilized to update the norm of NN's weight vector.
The highlight is that the minimal-learning-parameter technique is applied on the dynamics and the simple adaptive algorithm is easy to implement since the online updating computation burden is greatly reduced.
Different from previous design using back-stepping related technique, this paper analyzed the tracking control without back-stepping where the controller is synthesized with high gain observer and minimal-learning-parameter technique.
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