12 results on '"Wang, Le Yi"'
Search Results
2. Stochastic Adaptive Optimization With Dithers.
- Author
-
Xie, Siyu, Liang, Shu, Wang, Le Yi, Yin, George, and Chen, Wen
- Subjects
MATHEMATICAL optimization ,PARAMETER identification ,STOCHASTIC processes ,PARAMETER estimation ,NOISE measurement - Abstract
Optimization methods are essential and have been used extensively in a broad spectrum of applications. Most existing literature on optimization algorithms does not consider systems that involve unknown system parameters. This article studies a class of stochastic adaptive optimization problems in which identification of unknown parameters and search for the optimal solutions must be performed simultaneously. Due to a fundamental conflict between parameter identifiability and optimality in such problems, we introduce a method of adding stochastic dither signals into the system, which provide sufficient excitation for estimating the unknown parameters, leading to convergent adaptive optimization algorithms. Joint identification and optimization algorithms are developed and their simultaneous convergence properties of parameter estimation and optimization variable updates are proved. Under both noise-free and noisy observations, the corresponding convergence rates are established. The main results of this article reveal certain fundamental relationships and tradeoff among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy, and convergence rates. Simulation case studies are used to illustrate the adaptive optimization algorithms and their main properties. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Online parameter estimation of PMDC motors using quantized output observations.
- Author
-
Obeidat, Mohammad A, Wang, Le Yi, and Lin, Feng
- Abstract
Obtaining the dynamic behavior of permanent magnet direct current (PMDC) motors during operation is of essential importance for control adaptation, condition monitoring, and diagnosis. Quantized observations stem from either using low-cost sensors or using communication channels in remote control applications. In this paper, new estimation algorithms are developed to identify the motor speed and estimate the model parameters of PMDC motors using quantized output observations. This technique provides accurate estimation with lower costs on sensors or reduced communication resources. To validate the proposed estimator, periodic input dithers are used, and estimator accuracy and performance are demonstrated by simulation. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
4. Parameter estimation in systems with binary-valued observations and structural uncertainties.
- Author
-
Kan, Shaobai, Yin, G., and Wang, Le Yi
- Subjects
SYSTEM analysis ,PARAMETER estimation ,UNCERTAINTY (Information theory) ,STRUCTURAL analysis (Engineering) ,SYSTEM identification ,MATHEMATICAL bounds ,NUMERICAL analysis - Abstract
This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
5. Sign-Regressor Adaptive Filtering Algorithms for Markovian Parameters.
- Author
-
Yin, G. George, Hashemi, Araz, and Wang, Le Yi
- Subjects
ADAPTIVE filters ,MEAN square algorithms ,MATHEMATICS terminology ,MARKOV processes ,PARAMETER estimation ,DIFFERENTIAL equations ,MATHEMATICAL models - Abstract
This work is devoted to analyzing adaptive filtering algorithms with the use of sign-regressor for randomly time-varying parameters (a discrete-time Markov chain). In accordance with different adaption and transition rates, we analyze the corresponding asymptotic properties of the algorithms. When the adaptation rate is in line with the transition rate, we obtain a limit of a Markov switched differential equation. When the Markov chain is slowly changing the parameter process is almost a constant, and we derive a limit differential equation. When the Markov chain is fast varying, the limit system is again a differential equation that is an average with respect to the stationary distribution of the Markov chain. In addition to the limit dynamic systems, we obtain asymptotic properties of centered and scaled tracking errors. We obtain mean square errors to illustrate the dependence on the stepsize as well as on the transition rate. The limit distributions in terms of scaled errors are studied by examining certain centered and scaled error sequences. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
6. Accurate Probabilistic Characterization of Battery Estimates by Using Large Deviation Principles for Real-Time Battery Diagnosis.
- Author
-
Chen, Ziqiang, Wang, Le Yi, Yin, George, Lin, Feng, and Wang, Caisheng
- Subjects
- *
ELECTRIC batteries research , *QUANTITATIVE research , *ERROR analysis in mathematics , *ASYMPTOTIC distribution , *STOCHASTIC analysis , *BATTERY management systems - Abstract
Reliability of battery diagnosis depends on accurate estimation of the state of charge (SOC) and battery characterizing parameters including maximum capacity, internal impedance, polarization coefficients, and their probabilistic characterizations. This paper develops a framework that employs real-time operating data to estimate jointly the SOC and parameters, performs statistical analysis to derive quantitative diagnostic procedures with error analysis. Convergence of the algorithms, asymptotic distributions, and diagnosis reliability analysis are performed rigorously by using stochastic differential equations, central limit theorems, and large deviations principles. Simulated case studies and experimental data are used to illustrate the diagnosis algorithms and their capabilities. Experimental studies are conducted to verify the results. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
- View/download PDF
7. Real-Time Parameter Estimation of PMDC Motors Using Quantized Sensors.
- Author
-
Obeidat, Mohammad A., Wang, Le Yi, and Lin, Feng
- Subjects
- *
REAL-time computing , *PERMANENT magnets , *DIRECT currents , *ELECTRIC motors , *PARAMETER estimation , *SIGNAL quantization - Abstract
Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
8. Integrated System Identification and State-of-Charge Estimation of Battery Systems.
- Author
-
Liu, Lezhang, Wang, Le Yi, Chen, Ziqiang, Wang, Caisheng, Lin, Feng, and Wang, Hongbin
- Subjects
- *
ELECTRIC batteries , *SYSTEM identification , *PARAMETER estimation , *MEASUREMENT errors , *NONLINEAR systems , *MATHEMATICAL models , *ELECTRIC discharges , *SYSTEMS on a chip - Abstract
Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, SOC estimation has remained a challenging task. This is further compounded by the fact that battery characteristic model parameters change with time and operating conditions. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. The results are further expanded to derive an integrated algorithm to identify model parameters and initial SOC jointly. Simulations were performed to illustrate the capability and utility of the algorithms. Experimental verifications are conducted on Li-ion battery packs of different capacities under different load profiles. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
9. Enhanced Identification of Battery Models for Real-Time Battery Management.
- Author
-
Sitterly, Mark, Wang, Le Yi, Yin, G. George, and Wang, Caisheng
- Abstract
Renewable energy generation, vehicle electrification, and smart grids rely critically on energy storage devices for enhancement of operations, reliability, and efficiency. Battery systems consist of many battery cells, which have different characteristics even when they are new, and change with time and operating conditions due to a variety of factors such as aging, operational conditions, and chemical property variations. Their effective management requires high fidelity models. This paper aims to develop identification algorithms that capture individualized characteristics of each battery cell and produce updated models in real time. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares methods will encounter identification bias. This paper devises modified model structures and identification algorithms to resolve these issues. System identifiability, algorithm convergence, identification bias, and bias correction mechanisms are rigorously established. A typical battery model structure is used to illustrate utilities of the methods. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
- Full Text
- View/download PDF
10. Identification of Wiener systems with binary-valued output observations
- Author
-
Zhao, Yanlong, Wang, Le Yi, Yin, G. George, and Zhang, Ji-Feng
- Subjects
- *
ESTIMATION theory , *PARAMETER estimation , *STOCHASTIC systems , *PINACEAE - Abstract
Abstract: This work is concerned with identification of Wiener systems whose outputs are measured by binary-valued sensors. The system consists of a linear FIR (finite impulse response) subsystem of known order, followed by a nonlinear function with a known parametrization structure. The parameters of both linear and nonlinear parts are unknown. Input design, identification algorithms, and their essential properties are presented under the assumptions that the distribution function of the noise is known and the nonlinearity is continuous and invertible. It is shown that under scaled periodic inputs, identification of Wiener systems can be decomposed into a finite number of core identification problems. The concept of joint identifiability of the core problem is introduced to capture the essential conditions under which the Wiener system can be identified with binary-valued observations. Under scaled full-rank conditions and joint identifiability, a strongly convergent algorithm is constructed. The algorithm is shown to be asymptotically efficient for the core identification problem, hence achieving asymptotic optimality in its convergence rate. For computational simplicity, recursive algorithms are also developed. [Copyright &y& Elsevier]
- Published
- 2007
- Full Text
- View/download PDF
11. Asymptotically efficient parameter estimation using quantized output observations
- Author
-
Wang, Le Yi and Yin, G. George
- Subjects
- *
PARAMETER estimation , *ALGORITHMS , *SYSTEM identification , *DETECTORS - Abstract
Abstract: This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramér–Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank. [Copyright &y& Elsevier]
- Published
- 2007
- Full Text
- View/download PDF
12. Identification of linear continuous-time systems under irregular and random output sampling.
- Author
-
Mu, Biqiang, Guo, Jin, Wang, Le Yi, Yin, George, Xu, Lijian, and Zheng, Wei Xing
- Subjects
- *
CONTINUOUS time systems , *LINEAR systems , *STATISTICAL sampling , *PARAMETER estimation , *COMPUTER algorithms - Abstract
This paper considers the problem of identifiability and parameter estimation of single-input–single-output, linear, time-invariant, stable, continuous-time systems under irregular and random sampling schemes. Conditions for system identifiability are established under inputs of exponential polynomial types and a tight bound on sampling density. Identification algorithms of Gauss–Newton iterative types are developed to generate convergent estimates. When the sampled output is corrupted by observation noises, input design, sampling times, and convergent algorithms are intertwined. Persistent excitation (PE) conditions for strongly convergent algorithms are derived. Unlike the traditional identification, the PE conditions under irregular and random sampling involve both sampling times and input values. Under the given PE conditions, iterative and recursive algorithms are developed to estimate the original continuous-time system parameters. The corresponding convergence results are obtained. Several simulation examples are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.