A Reduced Gaussian Kernel Least-Mean-Square Algorithm for Nonlinear Adaptive Signal Processing

2018 ◽  
Vol 38 (1) ◽  
pp. 371-394 ◽  
Author(s):  
Yuqi Liu ◽  
Chao Sun ◽  
Shouda Jiang
Keyword(s):  
Signal Processing ◽  
Adaptive Signal Processing ◽  
Gaussian Kernel ◽  
Mean Square ◽  
Least Mean Square ◽  
Least Mean Square Algorithm ◽  
Kernel Least Mean Square ◽  
Adaptive Signal

Signature Analysis for Reciprocating Machinery with Adaptive Signal Processing

1991 ◽  
Vol 205 (5) ◽  
pp. 305-310 ◽  
Author(s):  
Q Zhuge ◽  
Y Lu
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Adaptive Signal Processing ◽  
Signature Analysis ◽  
Mean Square ◽  
Least Mean Square ◽  
Fast Fourier Transform Analysis ◽  
Least Mean Square Algorithm ◽  
Monitoring And Diagnostics ◽  
Adaptive Signal

The spectrum analysis by means of FFT (fast Fourier transform) analysis has been widely used in mechanical signature analysis. An alternative method is proposed in the paper for condition monitoring and diagnostics of reciprocating machines. A modified LMS (least mean square) algorithm is proposed in order to model the impulse-like signals with non-stationarity. Convergence of the algorithm is investigated. The approach presented is suitable to real-time monitoring during the process of working operation and fault diagnostics of reciprocating machines.

scholarly journals Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm

2012 ◽  
Vol 60 (5) ◽  
pp. 2208-2222 ◽  
Author(s):  
Wemerson D. Parreira ◽  
José Carlos M. Bermudez ◽  
Cédric Richard ◽  
Jean-Yves Tourneret
Keyword(s):  
Behavior Analysis ◽  
Gaussian Kernel ◽  
Mean Square ◽  
Least Mean Square ◽  
Stochastic Behavior ◽  
Least Mean Square Algorithm ◽  
Kernel Least Mean Square

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

scholarly journals Identification of Input Nonlinear Control Autoregressive Systems Using Fractional Signal Processing Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Naveed Ishtiaq Chaudhary ◽  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Muhammad Saeed Aslam
Keyword(s):  
Signal Processing ◽  
Nonlinear Control ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Mean Square ◽  
Least Mean Square ◽  
Third Order ◽  
Processing Approach ◽  
Kernel Least Mean Square ◽  
True Values

A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that the proposed FLMS algorithm provides most accurate and convergent results than those of VLMS and KLMS under different scenarios and by taking the low-to-high signal-to-noise ratio.

Sign in / Sign up

Export Citation Format

Share Document