Tensor-based adaptive filtering algorithms
Tensor-based signal processing methods are usually employed when dealing with
multidimensional data and/or systems with a large parameter space. In this paper, we …
multidimensional data and/or systems with a large parameter space. In this paper, we …
Optimized LMS algorithm for system identification and noise cancellation
Q Ling, MA Ikbal, P Kumar - Journal of Intelligent Systems, 2021 - degruyter.com
Optimization by definition is the action of making most effective or the best use of a resource
or situation and that is required almost in every field of engineering. In this work, the …
or situation and that is required almost in every field of engineering. In this work, the …
System identification based on tensor decompositions: A trilinear approach
The theory of nonlinear systems can currently be encountered in many important fields,
while the nonlinear behavior of electronic systems and devices has been studied for a long …
while the nonlinear behavior of electronic systems and devices has been studied for a long …
A recursive least-squares algorithm for the identification of trilinear forms
High-dimensional system identification problems can be efficiently addressed based on
tensor decompositions and modelling. In this paper, we design a recursive least-squares …
tensor decompositions and modelling. In this paper, we design a recursive least-squares …
Kalman-filter-based tension control design for industrial roll-to-roll system
H Hwang, J Lee, S Eum, K Nam - Algorithms, 2019 - mdpi.com
This paper presents a robust and precise tension control method for a roll-to-roll (R2R)
system. In R2R processing, robust and precise tension control is very important because …
system. In R2R processing, robust and precise tension control is very important because …
A Kalman filter for multilinear forms and its connection with tensorial adaptive filters
The Kalman filter represents a very popular signal processing tool, with a wide range of
applications within many fields. Following a Bayesian framework, the Kalman filter …
applications within many fields. Following a Bayesian framework, the Kalman filter …
Bayesian step least mean squares algorithm for Gaussian signals
PAC Lopes - IET Signal Processing, 2020 - Wiley Online Library
Selecting the step of the least mean squares (LMS) algorithm is an old problem. This study
uses a new approach to address this problem resulting in a new algorithm with excellent …
uses a new approach to address this problem resulting in a new algorithm with excellent …
[PDF][PDF] Tensor-Based Adaptive Filtering Algorithms. Symmetry 2021, 13, 481
Tensor-based signal processing methods are usually employed when dealing with
multidimensional data and/or systems with a large parameter space. In this paper, we …
multidimensional data and/or systems with a large parameter space. In this paper, we …
[PDF][PDF] Adaptive Algorithms for Multilinear in Parameters Structures
LM IANCU - 2019 - comm.pub.ro
Introduction imation, to identify this kind of spatiotemporal systems. Then, in the fifth chapter,
we focus on a slightly different case of supposedly linear systems that present small …
we focus on a slightly different case of supposedly linear systems that present small …