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

Identification of Time-Varying Time Constants of Thermocouple Sensors and Its Application to Temperature Measurement

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Kenneth Kar ◽  
Akshya K. Swain ◽  
Robert Raine
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Time Constant ◽  
Identification Problem ◽  
Basis Functions ◽  
New Technique ◽  
Ratio Test ◽  
Time Varying ◽  
Time Constants ◽  
Orthogonal Least Squares

The present study addresses the problem of estimating time-varying time constants associated with thermocouple sensors by a set of basis functions. By expanding each time-varying time constant onto a finite set of basis sequences, the time-varying identification problem reduces to a parameter estimation problem of a time-invariant system. The proposed algorithm, to be called as orthogonal least-squares with basis function expansion algorithm, combines the orthogonal least-squares algorithm with an error reduction ratio test to include significant basis functions into the model, which results in a parsimonious model structure. The performance of the method was compared with a linear Kalman filter. Simulations on engine data have demonstrated that the proposed method performs satisfactorily and is better than the Kalman filter. The new technique has been applied in a Stirling cycle compressor. The sinusoidal variations in time constant are tracked properly using the new technique, but the linear Kalman filter fails to do so. Both model validation and thermodynamic laws confirm that the new technique gives unbiased estimates and that the assumed thermocouple model is adequate.

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.

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.

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