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.

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 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.

Microclimate control of a greenhouse by adaptive Generalized Linear Quadratic strategy

2018 ◽  
Vol 11 (1) ◽  
pp. 377 ◽  
Author(s):  
Mohamed Essahafi ◽  
Mustapha Ait Lafkih
Keyword(s):  
Optimal Control ◽  
Least Squares ◽  
Recursive Least Squares ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Advanced Technique ◽  
Online Identification ◽  
State Models ◽  
The Cost ◽  
And Control

<p>To highlight the conceptual aspects related to the implementation of techniques optimal control in the form state, we present in this paper, the identification and control of the temperature and humidity of the air inside a greenhouse. Using respectively an online identification based on the recursive least squares with forgotten Factor method and the multivariable adaptive linear quadratic Gaussian approach which the advanced technique (LQG) is presented.  The design of this controller parameters is based on state models identified directly from measured greenhouse data. hence the performances of the controller developed are illustrated by different tests and simulations on identified models of a greenhouse. Discussions on the results obtained are then processed in the paper to show the effectiveness of the controller in terms of stability and optimization of the cost of control.</p>

scholarly journals Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruo-Nan Yang ◽  
Wei-Tao Zhang ◽  
Shun-Tian Lou
Keyword(s):  
Channel Estimation ◽  
Cost Function ◽  
Least Squares ◽  
Orthogonal Frequency Division Multiplexing ◽  
Blind Channel Estimation ◽  
Recursive Least Squares ◽  
Ofdm Systems ◽  
Blind Channel ◽  
Mimo Ofdm ◽  
The Cost

In order to track a changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is a priority to estimate channel impulse response adaptively. In this paper, we propose an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weigh the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of a third-order tensor, which consists of the weighted OFDM data symbols. By preserving the Khatri-Rao product, we used a recursive least squares solution to update the estimated subspace at each time instant, then the channel parameters can be estimated adaptively, and the algorithm achieves superior convergence performance. Simulation results validate the effectiveness of the proposed algorithm.

scholarly journals Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

2021 ◽  
Author(s):  
Xinghao Du ◽  
Jinhao Meng ◽  
Kailong Liu ◽  
Yingmin Zhang ◽  
Shunli Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Initial Value ◽  
Online Identification ◽  
Noise Disturbances ◽  
The Cost

Abstract Online parameter identification is essential for the accuracy of the battery Equivalent Circuit Model (ECM). The traditional Recursive Least Squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the Recursive Total Least Squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, fast convergence rate, and also robustness against noise corruption.

scholarly journals Recursive Least-Squares Estimation for Hammerstein Nonlinear Systems with Nonuniform Sampling

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiangli Li ◽  
Lincheng Zhou ◽  
Ruifeng Ding ◽  
Jie Sheng
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Identification Problem ◽  
Nonuniform Sampling ◽  
Recursive Least Squares ◽  
Separation Principle ◽  
Output Data ◽  
Auxiliary Model ◽  
Information Vector ◽  
Identification Model

This paper focuses on the identification problem of Hammerstein nonlinear systems with nonuniform sampling. Using the key-term separation principle, we present a discrete identification model with nonuniform sampling input and output data based on the frame period. To estimate parameters of the presented model, an auxiliary model-based recursive least-squares algorithm is derived by replacing the unmeasurable variables in the information vector with their corresponding recursive estimates. The simulation results show the effectiveness of the proposed algorithm.

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

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