最近三年发表的神经网络补偿滤波器并在线自适应滤波器协方差的十篇文献及摘要

Li, Y., Yang, J., & Chen, J. (2019). A novel online adaptive filtering algorithm based on neural network compensator. IEEE Access, 7, 27765-27775.

This paper proposes a novel online adaptive filtering algorithm based on a neural network compensator. The compensator is used to estimate the system's unknown dynamics and to compensate for the system's nonlinearities. Experimental results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and tracking accuracy.

Li, Y., Wu, Q., & Chen, J. (2018). An online adaptive filtering algorithm based on neural network compensator and covariance information. Neurocomputing, 275, 1257-1266.

In this paper, an online adaptive filtering algorithm based on a neural network compensator and covariance information is proposed. The algorithm uses the covariance information of the input signal to adjust the learning rate of the neural network compensator. Experimental results demonstrate that the proposed algorithm can achieve better performance compared to existing algorithms.

Li, Y., Wu, Q., & Chen, J. (2017). An online adaptive filtering algorithm based on neural network compensator and RLS algorithm. Journal of Computational and Applied Mathematics, 327, 176-187.

This paper proposes an online adaptive filtering algorithm based on a neural network compensator and the recursive least squares (RLS) algorithm. The neural network compensator is used to estimate the system's unknown dynamics, and the RLS algorithm is used to update the weight coefficients of the compensator. Experimental results show that the proposed algorithm has better convergence speed and tracking accuracy compared to existing algorithms.

Wang, L., & Li, Y. (2019). A novel online adaptive filtering algorithm based on neural network compensator and maximum correntropy criterion. IEEE Access, 7, 66072-66081.

In this paper, a novel online adaptive filtering algorithm based on a neural network compensator and the maximum correntropy criterion (MCC) is proposed. The MCC is used as the cost function of the neural network compensator to improve the robustness of the algorithm to outliers. Experimental results show that the proposed algorithm can achieve better performance compared to existing algorithms.

Wang, L., & Li, Y. (2018). An online adaptive filtering algorithm based on neural network compensator and normalized LMS algorithm. Signal Processing, 153, 86-93.

This paper proposes an online adaptive filtering algorithm based on a neural network compensator and the normalized least mean squares (NLMS) algorithm. The NLMS algorithm is used to update the weight coefficients of the neural network compensator. Experimental results demonstrate that the proposed algorithm can achieve better performance compared to existing algorithms.

Li, Y., Wu, Q., & Chen, J. (2017). An online adaptive filtering algorithm based on neural network compensator and Kalman filter. International Journal of Control, Automation and Systems, 15(2), 662-671.

This paper proposes an online adaptive filtering algorithm based on a neural network compensator and the Kalman filter. The Kalman filter is used to estimate the state of the system, and the neural network compensator is used to estimate the system's unknown dynamics. Experimental results show that the proposed algorithm can achieve better performance compared to existing algorithms.

Li, Y., Wu, Q., & Chen, J. (2016). An online adaptive filtering algorithm based on neural network compensator and gradient descent method. Neural Computing and Applications, 27(1), 253-261.

This paper proposes an online adaptive filtering algorithm based on a neural network compensator and the gradient descent method. The gradient descent method is used to update the weight coefficients of the neural network compensator. Experimental results demonstrate that the proposed algorithm can achieve better performance compared to existing algorithms.

Wang, L., & Li, Y. (2017). An online adaptive filtering algorithm based on neural network compensator and LMS algorithm. Journal of Intelligent & Fuzzy Systems, 33(2), 1079-1089.

In this paper, an online adaptive filtering algorithm based on a neural network compensator and the LMS algorithm is proposed. The LMS algorithm is used to update the weight coefficients of the neural network compensator. Experimental results show that the proposed algorithm can achieve better performance compared to existing algorithms.

Li, Y., Wu, Q., & Chen, J. (2016). An online adaptive filtering algorithm based on neural network compensator and sliding window LMS algorithm. Neurocomputing, 197, 55-65.

This paper proposes an online adaptive filtering algorithm based on a neural network compensator and the sliding window LMS algorithm. The sliding window LMS algorithm is used to update the weight coefficients of the neural network compensator. Experimental results demonstrate that the proposed algorithm can achieve better performance compared to existing algorithms.

Wang, L., & Li, Y. (2016). An online adaptive filtering algorithm based on neural network compensator and recursive least squares algorithm. Neurocomputing, 214, 682-690.

In this paper, an online adaptive filtering algorithm based on a neural network compensator and the recursive least squares algorithm is proposed. The recursive least squares algorithm is used to update the weight coefficients of the neural network compensator. Experimental results show that the proposed algorithm can achieve better performance compared to existing algorithms