KSIG: Improving the Convergence Rate in Adaptive Filtering Using Kernel Hilbert Space

2015 ◽  
Author(s):  
E. P. Da Silva ◽  
C.A. Estombelo-Montesco ◽  
Ewaldo Santana
Keyword(s):  
Hilbert Space ◽  
Convergence Rate ◽  
Adaptive Filtering

scholarly journals Affine Projection Algorithm Using Regressive Estimated Error

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Shu Zhang ◽  
Yongfeng Zhi
Keyword(s):  
Convergence Rate ◽  
Adaptive Filtering ◽  
Projection Algorithm ◽  
Error Signal ◽  
Affine Projection ◽  
Affine Projection Algorithm ◽  
Fast Convergence Rate ◽  
Simulation Results ◽  
The Stability ◽  
Estimated Error

An affine projection algorithm using regressive estimated error (APA-REE) is presented in this paper. By redefining the iterated error of the affine projection algorithm (APA), a new algorithm is obtained, and it improves the adaptive filtering convergence rate. We analyze the iterated error signal and the stability for the APA-REE algorithm. The steady-state weights of the APA-REE algorithm are proved to be unbiased and consist. The simulation results show that the proposed algorithm has a fast convergence rate compared with the APA algorithm.

Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering

2018 ◽  
pp. 245-251 ◽  
Author(s):  
J. S. Blandon ◽  
C. K. Valencia ◽  
A. Alvarez ◽  
J. Echeverry ◽  
M. A. Alvarez ◽  
...  
Keyword(s):  
Hilbert Space ◽  
Adaptive Filtering ◽  
Shape Classification

scholarly journals A Modified Quasi-Reversibility Method for a Class of Ill-Posed Cauchy Problems

2007 ◽  
Vol 14 (4) ◽  
pp. 627-642 ◽  
Author(s):  
Nadjib Boussetila ◽  
Faouzia Rebbani
Keyword(s):  
Hilbert Space ◽  
Cauchy Problem ◽  
Linear Operator ◽  
Convergence Rate ◽  
A Priori ◽  
Cauchy Problems ◽  
Unbounded Linear Operator ◽  
Ill Posed ◽  
New Perturbation ◽  
Problem Data

Abstract The goal of this paper is to present some extensions of the method of quasi-reversibility applied to an ill-posed Cauchy problem associated with an unbounded linear operator in a Hilbert space. The key point to our proof is the use of a new perturbation to construct a family of regularizing operators for the considered problem. We show the convergence of this method, and we estimate the convergence rate under a priori regularity assumptions on the problem data.

Hilbert Space

1993 ◽  
Author(s):  
J. R. Retherford
Keyword(s):  
Hilbert Space
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