A novel wavelet network based adaptive equalizer (WNBAE) is presented and the structure and stochastic gradient learning algorithm is given. The proposed WNBAE has better performance than that of the conventional lin...A novel wavelet network based adaptive equalizer (WNBAE) is presented and the structure and stochastic gradient learning algorithm is given. The proposed WNBAE has better performance than that of the conventional linear transversal equalizer based on the LMS and the RLS algorithms, as well as that of the decision feedback equalizer based on the RLS algorithm, especially for MQAM digital communication reception systems over the nonlinear channels. In addition, it outperforms the BP neural network based adaptive equalizer slightly. However, it has a slow convergence rate and a high computational complexity. Several simulations are performed to evaluate the behavior of the WNBAE.展开更多
To deal with the high nonlinearities and strong couplings in the transition stage of tailsitter aircraft,an adaptive gainscheduling controller is proposed by combining the guardian maps theory and H∞control theory.Th...To deal with the high nonlinearities and strong couplings in the transition stage of tailsitter aircraft,an adaptive gainscheduling controller is proposed by combining the guardian maps theory and H∞control theory.This method is applied to track the flightpath angle of the transition stage of tailsitter aircraft,and compared with the linear quadratic regulator(LQR)method based on traditional gain scheduling.Simulation results show that the controller based on the guardian maps theory can autonomously schedule the appropriate control parameters and accomplish the stable transition.Besides,the proposed method shows better tracking performance than the LQR method based on traditional gain scheduling.展开更多
文摘A novel wavelet network based adaptive equalizer (WNBAE) is presented and the structure and stochastic gradient learning algorithm is given. The proposed WNBAE has better performance than that of the conventional linear transversal equalizer based on the LMS and the RLS algorithms, as well as that of the decision feedback equalizer based on the RLS algorithm, especially for MQAM digital communication reception systems over the nonlinear channels. In addition, it outperforms the BP neural network based adaptive equalizer slightly. However, it has a slow convergence rate and a high computational complexity. Several simulations are performed to evaluate the behavior of the WNBAE.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.NJ2018015)。
文摘To deal with the high nonlinearities and strong couplings in the transition stage of tailsitter aircraft,an adaptive gainscheduling controller is proposed by combining the guardian maps theory and H∞control theory.This method is applied to track the flightpath angle of the transition stage of tailsitter aircraft,and compared with the linear quadratic regulator(LQR)method based on traditional gain scheduling.Simulation results show that the controller based on the guardian maps theory can autonomously schedule the appropriate control parameters and accomplish the stable transition.Besides,the proposed method shows better tracking performance than the LQR method based on traditional gain scheduling.
