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This paper focuses on both the structure uncertainty and parameter uncertainty which have been widely explored in the literature of nonlinear system identification. An integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance.
Aiming at the uncertain nonlinear system with a dead zone input, a design method of adaptive neuro sliding mode control is presented to combine neural network theory with sliding mode control theory. RBF neural networks are used to realize modeling of nondeterministic and nonlinear system. Adaptive laws are derived based on Lyapunov stability theory which guarantees the stability of control system...
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