This paper gives a simple analysis of the method of using the Hopfield’s optimization neural network to solve the direction-of-arrival(DOA) estimation problem. Although this method can avoid the eigendecomposition of...This paper gives a simple analysis of the method of using the Hopfield’s optimization neural network to solve the direction-of-arrival(DOA) estimation problem. Although this method can avoid the eigendecomposition of data autocorrelation matrix and the orthogonality search of parameter space, theoretical analysis and computer simulation results show that the construction of the DOA cost function is incorrect on the condition that there is no constraint on the number of outputs of the network.展开更多
In the field of system identification, the coefficients of a digital filter, which are used to illustrate the transfer function of a system, are often fixed by known input and output signal sequences. The least mean s...In the field of system identification, the coefficients of a digital filter, which are used to illustrate the transfer function of a system, are often fixed by known input and output signal sequences. The least mean square (LMS) adaptive algorithm is often used for its small computational requirement and convergence performance.Generally, the input or the output signal sequence often contains directive current (DC) components. What is the effect of the DC component upon the adaptive algorithm? This is just the main objective of this note. Here we propose an improved LMS adaptive algorithm with varying stepsize, which is based on the DC components analyzing.展开更多
基金Supported by the National Natural Science Foundation of ChinaTrans-Century Training Program Foundation for the Talents by the State Education Commission of China
文摘This paper gives a simple analysis of the method of using the Hopfield’s optimization neural network to solve the direction-of-arrival(DOA) estimation problem. Although this method can avoid the eigendecomposition of data autocorrelation matrix and the orthogonality search of parameter space, theoretical analysis and computer simulation results show that the construction of the DOA cost function is incorrect on the condition that there is no constraint on the number of outputs of the network.
文摘In the field of system identification, the coefficients of a digital filter, which are used to illustrate the transfer function of a system, are often fixed by known input and output signal sequences. The least mean square (LMS) adaptive algorithm is often used for its small computational requirement and convergence performance.Generally, the input or the output signal sequence often contains directive current (DC) components. What is the effect of the DC component upon the adaptive algorithm? This is just the main objective of this note. Here we propose an improved LMS adaptive algorithm with varying stepsize, which is based on the DC components analyzing.
