scholarly journals A Kernel Recursive Maximum Versoria-Like Criterion Algorithm for Nonlinear Channel Equalization

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1067 ◽  
Author(s):  
Qishuai Wu ◽  
Yingsong Li ◽  
Wei Xue
Keyword(s):  
Adaptive Filtering ◽  
Impulse Noise ◽  
Channel Equalization ◽  
Mean Square ◽  
Least Mean Square ◽  
Mixed Mean ◽  
Non Gaussian ◽  
Kernel Least Mean Square ◽  
Nonlinear Adaptive Filtering ◽  
Better Than

In this paper, a kernel recursive maximum Versoria-like criterion (KRMVLC) algorithm has been constructed, derived, and analyzed within the framework of nonlinear adaptive filtering (AF), which considers the benefits of logarithmic second-order errors and the symmetry maximum-Versoria criterion (MVC) lying in reproducing the kernel Hilbert space (RKHS). In the devised KRMVLC, the Versoria approach aims to resist the impulse noise. The proposed KRMVLC algorithm was carefully derived for taking the nonlinear channel equalization (NCE) under different non-Gaussian interferences. The achieved results verify that the KRMVLC is robust against non-Gaussian interferences and performs better than those of the popular kernel AF algorithms, like the kernel least-mean-square (KLMS), kernel least-mixed-mean-square (KLMMN), and Kernel maximum Versoria criterion (KMVC).

scholarly journals Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels

2020 ◽  
Author(s):  
Patrick Medeiros De Luca ◽  
Wemerson Delcio Parreira
Keyword(s):  
Monte Carlo Simulation ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Linear Filter ◽  
Mean Square ◽  
Least Mean Square ◽  
Model Order ◽  
Different Types ◽  
Kernel Least Mean Square ◽  
Nonlinear Adaptive Filtering

The kernel least-mean-square (KLMS) algorithm is a popular algorithmin nonlinear adaptive filtering due to its simplicity androbustness. In kernel adaptive filtering, the statistics of the inputto the linear filter depends on the kernel and its parameters. Moreover,practical implementations on systems estimation require afinite non-linearity model order. In order to obtain finite ordermodels, many kernelized adaptive filters use a dictionary of kernelfunctions. Dictionary size also depends on the kernel and itsparameters. Therefore, KLMS may have different performanceson the estimation of a nonlinear system, the time of convergence,and the accuracy using a different kernel. In order to analyze theperformance of KLMS with different kernels, this paper proposesthe use of the Monte Carlo simulation of both steady-state and thetransient behavior of the KLMS algorithm using different types ofkernel functions and Gaussian inputs.

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

scholarly journals The Generalized Complex Kernel Least-Mean-Square Algorithm

2019 ◽  
Vol 67 (20) ◽  
pp. 5213-5222 ◽  
Author(s):  
Rafael Boloix-Tortosa ◽  
Juan Jose Murillo-Fuentes ◽  
Sotirios A. Tsaftaris
Keyword(s):  
Mean Square ◽  
Least Mean Square ◽  
Generalized Complex ◽  
Least Mean Square Algorithm ◽  
Kernel Least Mean Square

Implementation of Adaptive Noise Canceller System for Audio-Related Applications

2013 ◽  
Vol 3 (4) ◽  
pp. 51-67
Author(s):  
Swati S. Godbole ◽  
Sanjay B. Pokle
Keyword(s):  
Adaptive Filtering ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Noise Component ◽  
Wide Range ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Filtering Techniques

This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

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