scholarly journals A Recursive Least-Squares Algorithm for the Identification of Trilinear Forms

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 135
Author(s):  
Camelia Elisei-Iliescu ◽  
Laura-Maria Dogariu ◽  
Constantin Paleologu ◽  
Jacob Benesty ◽  
Andrei-Alexandru Enescu ◽  
...  
Keyword(s):  
Least Squares ◽  
Impulse Response ◽  
Recursive Least Squares ◽  
Dimensional System ◽  
Third Order ◽  
Rls Algorithm ◽  
Tensor Decompositions ◽  
Rank One ◽  
The Individual ◽  
Least Mean Squares Algorithm

High-dimensional system identification problems can be efficiently addressed based on tensor decompositions and modelling. In this paper, we design a recursive least-squares (RLS) algorithm tailored for the identification of trilinear forms, namely RLS-TF. In our framework, the trilinear form is related to the decomposition of a third-order tensor (of rank one). The proposed RLS-TF algorithm acts on the individual components of the global impulse response, thus being efficient in terms of both performance and complexity. Simulation results indicate that the proposed solution outperforms the conventional RLS algorithm (which handles only the global impulse response), but also the previously developed trilinear counterparts based on the least-mean- squares algorithm.

scholarly journals Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

2021 ◽  
Vol 11 (18) ◽  
pp. 8656
Author(s):  
Ionuț-Dorinel Fîciu ◽  
Cristian-Lucian Stanciu ◽  
Cristian Anghel ◽  
Camelia Elisei-Iliescu
Keyword(s):  
System Identification ◽  
Least Squares ◽  
Adaptive Systems ◽  
Stable Solution ◽  
Tensor Decomposition ◽  
Low Complexity ◽  
Recursive Least Squares ◽  
Rls Algorithm ◽  
Multilinear Forms ◽  
Single Output

Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.

scholarly journals A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1580
Author(s):  
Junseok Lim ◽  
Keunhwa Lee ◽  
Seokjin Lee
Keyword(s):  
Computational Complexity ◽  
Impulse Response ◽  
Recursive Least Squares ◽  
Mean Square ◽  
L1 Norm ◽  
Actual System ◽  
Rls Algorithm ◽  
Simulation Results ◽  
Norm Penalty ◽  
True System

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.

scholarly journals Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 237
Author(s):  
Ionuț-Dorinel Fîciu ◽  
Cristian-Lucian Stanciu ◽  
Camelia Elisei-Iliescu ◽  
Cristian Anghel
Keyword(s):  
Least Squares ◽  
Tensor Decomposition ◽  
Identification Problem ◽  
Low Complexity ◽  
Recursive Least Squares ◽  
Dimensional System ◽  
Multilinear Forms ◽  
Coordinate Descent Algorithm ◽  
Noise Simulation ◽  
The Cost

The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.

The Fuzzy CMAC Based on RLS Algorithm

2013 ◽  
Vol 432 ◽  
pp. 478-482
Author(s):  
Hui Cheng
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Complex Systems ◽  
Least Squares ◽  
Potential Application ◽  
Recursive Least Squares ◽  
Rls Algorithm ◽  
Generalization Ability ◽  
Fuzzy Cmac

In this paper, the structure of the fuzzy crebellar model articulation controller (FCMAC) neural network was discussed. The FCMAC can improve the accuracy of the CMAC. It also has excellent generalization ability and fault-tolerance ability. The recursive least squares (RLS) algorithm was introduced into the FCMAC. The FCMAC based on RLS algorithm has potential application prospect in the research of modeling and emulation on the complex systems.

scholarly journals Parameter Estimation of Induction Machine at Standstill Using Two-Stage Recursive Least Squares Method

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jinliang Zhang ◽  
Longyun Kang ◽  
Lingyu Chen ◽  
Zhihui Xu
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Least Squares Method ◽  
Induction Machine ◽  
Recursive Least Squares ◽  
Two Stage ◽  
Electric Parameter ◽  
Rls Algorithm ◽  
Effective Implementation ◽  
Recursive Least Squares Method

This paper presents a two-stage recursive least squares (TSRLS) algorithm for the electric parameter estimation of the induction machine (IM) at standstill. The basic idea of this novel algorithm is to decouple an identifying system into two subsystems by using decomposition technique and identify the parameters of each subsystem, respectively. The TSRLS is an effective implementation of the recursive least squares (RLS). Compared with the conventional (RLS) algorithm, the TSRLS reduces the number of arithmetic operations. Experimental results verify the effectiveness of the proposed TSRLS algorithm for parameter estimation of IMs.

scholarly journals Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

2018 ◽  
Vol 160 ◽  
pp. 01001
Author(s):  
Chen Chen ◽  
Run Min ◽  
Qiaoling Tong ◽  
Shifei Tao ◽  
Dian Lyu ◽  
...  
Keyword(s):  
Least Squares ◽  
Low Cost ◽  
Sampling Rate ◽  
Boost Converter ◽  
Identification Accuracy ◽  
Recursive Least Squares ◽  
Control Technique ◽  
Forgetting Factor ◽  
Rls Algorithm ◽  
Variable Forgetting Factor

The control performance of boost converter suffers from the variations of important component parameters, such as inductance and capacitance. In this paper, an online inductance and capacitance identification based on variable forgetting factor recursive least-squares (VFF-RLS) algorithm for boost converter is proposed. First, accurate inductance and capacitance identification models and the RLS algorithm are introduced. In order to balance the steady-state identification accuracy and parameter tracking ability, a forgetting factor control technique is investigated. By recovering system noise in the error signal of the algorithm, the value of forgetting factor is dynamically calculated. In addition, since the sampling rate is much lower than the existing identification methods, the proposed algorithm is practical for low-cost applications. Finally, the effectiveness of the proposed algorithm is verified by experiment. The experiment results show that the algorithm has good performance in tracking inductance and capacitance variations.

Change in Impulse Response of Heart Rate to Instantaneous Lung Volume

1994 ◽  
Vol 33 (01) ◽  
pp. 20-21
Author(s):  
H. Mizuta ◽  
Y. Iida ◽  
J. P. Saul ◽  
R. J. Cohen ◽  
K. Yana
Keyword(s):  
Heart Rate ◽  
Impulse Response ◽  
Lung Volume ◽  
Real Data ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Time Data ◽  
Rls Algorithm ◽  
Stationary Conditions ◽  
Real Time Data Processing

Abstract:A method is presented that relates the heart rate variability (HRV) to the change in instantaneous lung volume (ILV) under non-stationary conditions. Methods utilizing a recursive least squares (RLS) algorithm and a modified Widrow LMS algorithm are proposed to keep track of changes in impulse response of HRV to ILV. Results are presented of real data analysis and a dedicated system is proposed utilizing DSP chips for the real time data processing.

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