A Kernel Recursive Maximum Versoria-Like Criterion Algorithm for Nonlinear Channel Equalization
نویسندگان
چکیده
منابع مشابه
Square Root Extended Kernel Recursive Least Squares Algorithm for Nonlinear Channel Equalization
This study presents a square root version of extended kernel recursive least square algorithm. Basically main idea is to overcome the divergence phenomena arise in the computation of weights of the extended kernel recursive least squares algorithm. Numerically stable givens orthogonal transformations are used to obtain the next iteration of the algorithm. The usefulness of the proposed algorith...
متن کاملNonlinear Channel Equalization Using Multilayer Perceptrons with Information-theoretic Criterion
The minimum error entropy criterion was recently suggested in adaptive system training as an alternative to the mean-square-error criterion, and it was shown to produce better results in many tasks. In this paper, we apply a multiplayer perceptron scheme trained with this information theoretic criterion to the problem of nonlinear channel equalization. In our simulations, we use a realistic non...
متن کاملKernel recursive maximum correntropy
Zongze Wu 1 , Jiahao Shi 1 , Xie Zhang 1 , Wentao Ma 2 , Badong Chen 2* , Senior Member, IEEE 1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, China 2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China * Fax: 86-29-82668672,Tel:86-29-82668802 ext. 8009, [email protected] Abstract—I...
متن کاملA truly recursive blind equalization algorithm
This paper describes a new adaptive blind equalization algorithm based on a truly IIR structure that enables the correction of ISI over severely distorted channels. The recursive feedback filter is in lattice form to allow an easy monitoring of the filter stability. During blind training, the adaptation of the equalizer is carried out via the usual stochastic gradient algorithm by minimizing th...
متن کاملRecursive Generalized Maximum Correntropy Criterion Algorithm with Sparse Penalty Constraints for System Identification
To address sparse system identification problem in non-Gaussian impulsive noise environment, the recursive generalized maximum correntropy criterion (RGMCC) algorithm with sparse penalty constraints is proposed to combat impulsive-inducing instability. Specifically, a recursive algorithm based on the generalized correntropy with a forgetting factor of error is developed to improve the performan...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Symmetry
سال: 2019
ISSN: 2073-8994
DOI: 10.3390/sym11091067