Next Article in Journal
Sensor Fusion Based Pipeline Inspection for the Augmented Reality System
Previous Article in Journal
A New Stability Theory for Grünwald–Letnikov Inverse Model Control in the Multivariable LTI Fractional-Order Framework
Open AccessArticle

The Cauchy Conjugate Gradient Algorithm with Random Fourier Features

by Xuewei Huang 1,2, Shiyuan Wang 1,2,* and Kui Xiong 1,2
1
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(10), 1323; https://doi.org/10.3390/sym11101323
Received: 23 September 2019 / Revised: 10 October 2019 / Accepted: 17 October 2019 / Published: 22 October 2019
Random Fourier mapping (RFM) in kernel adaptive filters (KAFs) provides an efficient method to curb the linear growth of the dictionary by projecting the original input data into a finite-dimensional space. The commonly used measure in RFM-based KAFs is the minimum mean square error (MMSE), which causes performance deterioration in the presence of non-Gaussian noises. To address this issue, the minimum Cauchy loss (MCL) criterion has been successfully applied for combating non-Gaussian noises in KAFs. However, these KAFs using the well-known stochastic gradient descent (SGD) optimization method may suffer from slow convergence rate and low filtering accuracy. To this end, we propose a novel robust random Fourier features Cauchy conjugate gradient (RFFCCG) algorithm using the conjugate gradient (CG) optimization method in this paper. The proposed RFFCCG algorithm with low complexity can achieve better filtering performance than the KAFs with sparsification, such as the kernel recursive maximum correntropy algorithm with novelty criterion (KRMC-NC), in stationary and non-stationary environments. Monte Carlo simulations conducted in the time-series prediction and nonlinear system identification confirm the superiorities of the proposed algorithm. View Full-Text
Keywords: random Fourier mapping; minimum Cauchy loss; non-Gaussian noise; conjugate gradient; complexity random Fourier mapping; minimum Cauchy loss; non-Gaussian noise; conjugate gradient; complexity
Show Figures

Figure 1

MDPI and ACS Style

Huang, X.; Wang, S.; Xiong, K. The Cauchy Conjugate Gradient Algorithm with Random Fourier Features. Symmetry 2019, 11, 1323.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop