scholarly journals A unified identification algorithm of FIR systems based on binary observations with time-varying thresholds

Automatica ◽  
2022 ◽  
Vol 135 ◽  
pp. 109990
Author(s):  
Ying Wang ◽  
Yanlong Zhao ◽  
Ji-Feng Zhang ◽  
Jin Guo
Keyword(s):  
Identification Algorithm ◽  
Time Varying ◽  
Fir Systems

scholarly journals Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fengxia Xu ◽  
Yao Cheng ◽  
Hongliang Ren ◽  
Shili Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Delay System ◽  
Network Control ◽  
Identification Algorithm ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Time Varying Delay ◽  
Adaptive Neural Network Control ◽  
Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

A Robust Time-Varying Identification Algorithm Using Basis Functions

2003 ◽  
Vol 31 (7) ◽  
pp. 840-853 ◽  
Author(s):  
Rui Zou ◽  
Hengliang Wang ◽  
Ki H. Chon
Keyword(s):  
Basis Functions ◽  
Identification Algorithm ◽  
Time Varying

scholarly journals Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haotian Zhou ◽  
Kaiping Yu ◽  
Yushu Chen ◽  
Rui Zhao ◽  
Yunhe Bai
Keyword(s):  
Parameter Identification ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Post Process

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios.

Estimate of Attenuation Coefficient for Ultrasonic Tissue Characterization Using Time-Varying State-Space Model

1988 ◽  
Vol 10 (2) ◽  
pp. 90-109 ◽  
Author(s):  
Li Y. Shih ◽  
Casper W. Barnes ◽  
Leonard A. Ferrari
Keyword(s):  
State Space ◽  
Attenuation Coefficient ◽  
Tissue Characterization ◽  
Wigner Distribution ◽  
Output Coupling ◽  
Pathological State ◽  
Identification Algorithm ◽  
Time Varying ◽  
Pulse Echo

The images generated from ultrasound pulse-echo signals have long been used to aid clinical diagnosis. Recently, there has been a growing interest in quantitatively determining the acoustic parameters of the tissue as a means of classification and diagnosis. For example, the frequency-dependent attenuation is known to be correlated with different diseases in the liver. In this paper we introduce a new technique for estimating the attenuation coefficient. The effect of attenuation on an interrogating signal with a gaussian-shaped spectrum can be obtained by studying the Wigner distribution of reflected rf data based on a one-dimensional signal model. We show that under the condition that the attenuation varies linearly with frequency, the spectral mean of the reflected signal decreases linearly with time. The estimation algorithm models the pulse-echo signal as the output of a second-order time-varying state-space innovations model driven by white noise. The state coupling matrix A and the output coupling vector C vary with time in a known fashion; moreover, they are also functions of an unknown constant parameter θ. The attenuation coefficient, which is one of the elements of θ, can be estimated directly using a recursive system identification algorithm. The algorithm was verified using both computer-generated synthetic data and in-vivo liver data of known diagnosis. The results show correlation between the estimated parameter and the pathological State Of the tissue.

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

2021 ◽  
pp. 147592172110360
Author(s):  
Erhua Zhang ◽  
Di Wu ◽  
Deshan Shan
Keyword(s):  
System Identification ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Advanced Technique ◽  
Stabilization Diagram ◽  
Bridge Structures ◽  
Tensor Subspace

Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.

Identification of Linear Time-Varying Parameters of Nonstationary Systems

2019 ◽  
Vol 20 (5) ◽  
pp. 269-265
Author(s):  
V. T. Le ◽  
M. M. Korotina ◽  
A. A. Bobtsov ◽  
S. V. Aranovskiy ◽  
Q. D. Vo
Keyword(s):  
Linear Time ◽  
Control Object ◽  
Time Interval ◽  
Identification Algorithm ◽  
Time Varying ◽  
State Variables ◽  
Unknown Parameters ◽  
Technical Problems ◽  
True Values ◽  
Stationary Control

The paper considers the identification algorithm for unknown parameters of linear non-stationary control objects. It is assumed that only the object output variable and the control signal are measured (but not their derivatives or state variables) and unknown parameters are linear functions or their derivatives are piecewise constant signals. The derivatives of non-stationary parameters are supposed to be unknown constant numbers on some time interval. This assumption for unknown parameters is not mathematical abstraction because in most electromechanical systems parameters are changing during the operation. For example, the resistance of the rotor is linearly changing, because the resistance of the rotor depends on the temperature changes of the electric motor in operation mode. This paper proposes an iterative algorithm for parameterization of the linear non-stationary control object using stable LTI filters. The algorithm leads to a linear regression model, which includes time-varying and constant (at a certain time interval) unknown parameters. For this model, the dynamic regressor extension and mixing (DREM) procedure is applied. If the persistent excitation condition holds, then, in the case the derivative of each parameter is constant on the whole time interval, DREM provides the convergence of the estimates of configurable parameters to their true values. In the case of a finite time interval, the estimates convergence in a certain region. Unlike well-known gradient approaches, using the method of dynamic regressor extension and mixing allows to improve the convergence speed and accuracy of the estimates to their true values by increasing the coefficients of the algorithm. Additionally, the method of dynamic regressor extension and mixing ensures the monotony of the processes, and this can be useful for many technical problems.

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