典型文献
Adaptive Generalized Eigenvector Estimating Algorithm for Hermitian Matrix Pencil
文献摘要：
Generalized eigenvector plays an essential role in the signal processing field. In this paper, we present a novel neural network learning algorithm for estimating the generalized eigen-vector of a Hermitian matrix pencil. Differently from some tradi-tional algorithms, which need to select the proper values of learn-ing rates before using, the proposed algorithm does not need a learning rate and is very suitable for real applications. Through analyzing all of the equilibrium points, it is proven that if and only if the weight vector of the neural network is equal to the gen-eralized eigenvector corresponding to the largest generalized eigenvalue of a Hermitian matrix pencil, the proposed algorithm reaches to convergence status. By using the deterministic discrete-time (DDT) method, some convergence conditions, which can be satisfied with probability 1, are also obtained to guarantee its con-vergence. Simulation results show that the proposed algorithm has a fast convergence speed and good numerical stability. The real application demonstrates its effectiveness in tracking the optimal vector of beamforming.
文献关键词：
中图分类号：
作者姓名：
Yingbin Gao
作者机构：
54th Research Institute of China Electronics Technology Group Corporation(CETC54),Shijiazhuang 050050,China
文献出处：
引用格式：
[1]Yingbin Gao-.Adaptive Generalized Eigenvector Estimating Algorithm for Hermitian Matrix Pencil)[J].自动化学报（英文版）,2022(11):1967-1979
A类：
Eigenvector,Pencil
B类：
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AB值：
0.569258
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