Online adaptive bank of recursive least square estimators for slowly time-varying and abruptly changing systems

2016 ◽  
Author(s):  
Yoshiaki Sakakura ◽  
Chiharu Yamano
Keyword(s):  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Least Square Estimators

scholarly journals Windowed Least Square Algorithm Based PMSM Parameters Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Song Wang
Keyword(s):  
Embedded System ◽  
Window Size ◽  
Parameters Estimation ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Permanent Magnet Synchronous Motors ◽  
Practical Applications ◽  
Controlling System ◽  
System Resource

Stator resistance and inductances ind-axis andq-axis of permanent magnet synchronous motors (PMSMs) are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS) to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS) and extended Kalman filter (EKF). It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.

A Method for Modal Parameter Identification of Time Varying Vibration Systems

2012 ◽  
Vol 479-481 ◽  
pp. 688-693
Author(s):  
Zi Ying Wu ◽  
Kun Shi
Keyword(s):  
Linear Time ◽  
Least Square Method ◽  
Degree Of Freedom ◽  
Least Square ◽  
Modal Parameters ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Linear Time Invariant ◽  
Prony Method

In this paper a new time varying multivariate Prony (TVM-Prony) method is put forward to identify modal parameters of time varying (TV) multiple-degree-of-freedom systems from measured vibration responses. The proposed method is based on the classical Prony method that is often used to identify modal parameters of linear time invariant systems. The main advantage of the propose approach is that it can analyze multi-dimensional nonstationary signals simultaneously. A modified recursive least square method based on the traditional one is presented to determine the TV coefficient matrices of the multivariate parametric model established in the proposed method. The efficiency and accuracy of the identification approach is demonstrated by a numerical example, in which a TV mass-string system with three-degree-of-freedom is investigated. Satisfied results are obtained.

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

Combining CEEMD and recursive least square for the extraction of time-varying seismic wavelets

2019 ◽  
Vol 170 ◽  
pp. 103854
Author(s):  
Peng Zhang ◽  
Yongshou Dai ◽  
Hongqian Zhang ◽  
Chunxian Wang ◽  
Yuhan Zhang
Keyword(s):  
Least Square ◽  
Time Varying ◽  
Recursive Least Square

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

Modal Parameter Identification of Time-Varying Systems Using the Time-Varying Multivariate Autoregressive Model

2005 ◽  
Author(s):  
Lilan Liu ◽  
Hongzhao Liu ◽  
Ziying Wu ◽  
Daning Yuan ◽  
Pengfei Li
Keyword(s):  
Parameter Identification ◽  
Linear Time ◽  
Damping Ratio ◽  
Least Square Method ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Multivariate Autoregressive

A new time-varying multivariate autoregressive (TVMAR) model method for modal parameter identification of linear time-varying (TV) systems with multi-output is introduced. Besides, a modified recursive least square method based on the traditional one is presented to determine the coefficient matrices of the TVMAR model. In the proposed method, multi-dimensional nonstationary response signals of the vibrating system can be processed simultaneously. Not only the TV modal frequency and damping ratio of the system, but also the changing behavior of the mode shape in the course of vibration are identified by the proposed procedure. Numerical simulations, in which a three-degree-of-freedom system with TV stiffness is respectively subjected to impulse excitation and white noise excitation, are presented. The validity and accuracy of the method are demonstrated by the good simulation results.

MODEL-FREE METHOD FOR TIME VARYING DYNAMIC MEASUREMENTS IN CONTROL SYSTEM

2020 ◽  
Vol 70 (3) ◽  
pp. 51-60
Author(s):  
Miroslava Baraharska ◽  
Tsonyo Slavov ◽  
Ivan Markovsky
Keyword(s):  
Control System ◽  
Mass Measurement ◽  
Least Square Method ◽  
Information Criteria ◽  
Least Square ◽  
Time Varying ◽  
Dynamic Measurements ◽  
Recursive Least Square ◽  
Model Order Selection ◽  
Model Free

In this paper, a model-free method for time-varying dynamic measurements in a control system is presented. As an example, the dynamic mass-measurement process is examined. The method is based on the on-line estimation of time-varying parameters of autoregressive model by a recursive least square method with a constant trace of the covariance matrix. The model order selection is performed by Akaike’s information criteria. The performance of the method with respect to the variance of measurement noise is empirically tested by simulation experiments. For the aim of comparison, the Kalman filter for estimation of unknown measurement is designed. The simulation results show the advantage of the model-free method.

Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India

2019 ◽  
Vol 118 (3) ◽  
pp. 137-152
Author(s):  
A. Shanthi ◽  
R. Thamilselvan
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Research Work ◽  
Futures Market ◽  
Least Square ◽  
Time Varying ◽  
Hedge Ratio ◽  
Hedging Effectiveness ◽  
Out Of Sample ◽  
Optimal Hedge

The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

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