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Ship heading sliding mode tracking control algorithm was investigated based on the minimum parameter learning algorithm of Radial Basis Function (RBF) neural network. RBF neural network has been used to approach the uncertainty function of ship nonlinear motion system and unknown external interference. In consideration that the RBF neural network weights is difficult to adjust quickly, the minimum...
To guarantee the safety of an unmanned aircraft on the accident of the control surface failure, an algorithm of fault detection and surface reconfiguration is necessary. As the neural network can approximates arbitrary nonlinear functions, the neural network NARMAX model is able to capture the nonlinear dynamics. The proposed neural network NARMAX model based complementary filter provides an efficient...
To deal with time-varying equations, a novel recurrent neural network, named varying-parameter convergentdifferential neural network (in short, VP-CDNN), is proposed, modeled and analyzed. It is designed by a matrix-valued error function and the design parameter is time-varying, which enables the VP-CDNN to have good convergence and robustness. For illustration and comparison, a scalar-valued error...
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