Xiafei Tang

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In this paper neural network (NN) is applied for rejecting periodic disturbances in output feedback nonlinear system. The NN adopted here is Adaptive Radial Basis Function Neural Network (ARBFNN). The parameters of the system, except the high gain frequency, and disturbance are assumed to be unknown. We also postulate that the uncertainty of the output(More)
MLE(Maximum Likelihood Estimation) is widely applied in system identification because of its consistency, asymptotic efficiency and sufficiency. However gradient-based optimization of the likelihood function might end up in local convergence. To overcome this difficulty, the non-local-minimum conditions are very useful. Here we suggest a heuristic method of(More)
This study presents a periodic disturbance rejection method for a class of nonlinear systems with the input weighting vector in the proportional nonlinear form. Especially, the periodic disturbance does not match with the system input. A neural network approximator is employed for the estimation of the ideal feedforward control input that tackles the(More)
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