Constrained receding-horizon experiment design and parameter estimation in the presence of poor initial conditions

AIChE Journal ◽  
2010 ◽  
Vol 57 (10) ◽  
pp. 2808-2820 ◽  
Author(s):  
Yijia Zhu ◽  
Biao Huang
Keyword(s):  
Parameter Estimation ◽  
Initial Conditions ◽  
Experiment Design ◽  
Receding Horizon

Receding horizon experiment design with application in SOFC parameter estimation

2010 ◽  
Vol 43 (5) ◽  
pp. 541-546 ◽  
Author(s):  
Barath Ram Jayasankar ◽  
Biao Huang ◽  
Amos Ben-Zvi
Keyword(s):  
Parameter Estimation ◽  
Experiment Design ◽  
Receding Horizon

scholarly journals Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter

2022 ◽  
Vol 109 ◽  
pp. 13-31
Author(s):  
Pavanraj H. Rangegowda ◽  
Jayaram Valluru ◽  
Sachin C. Patwardhan ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Receding Horizon ◽  
Nonlinear Kalman Filter

A New Cost Function for Parameter Estimation of Chaotic Systems Using Return Maps as Fingerprints

2014 ◽  
Vol 24 (10) ◽  
pp. 1450134 ◽  
Author(s):  
Sajad Jafari ◽  
Julien C. Sprott ◽  
Viet-Thanh Pham ◽  
S. Mohammad Reza Hashemi Golpayegani ◽  
Amir Homayoun Jafari
Keyword(s):  
Parameter Estimation ◽  
Cost Function ◽  
Chaotic Systems ◽  
Time Series Data ◽  
Initial Conditions ◽  
Model Systems ◽  
Series Data ◽  
Return Maps ◽  
Active Interest ◽  
The Time Domain

Estimating parameters of a model system using observed chaotic scalar time series data is a topic of active interest. To estimate these parameters requires a suitable similarity indicator between the observed and model systems. Many works have considered a similarity measure in the time domain, which has limitations because of sensitive dependence on initial conditions. On the other hand, there are features of chaotic systems that are not sensitive to initial conditions such as the topology of the strange attractor. We have used this feature to propose a new cost function for parameter estimation of chaotic models, and we show its efficacy for several simple chaotic systems.

Online Parameter Estimation of the Lankarani–Nikravesh Contact Force Model Using Two Different Methods

2016 ◽  
Vol 13 (04) ◽  
pp. 1641017 ◽  
Author(s):  
Jia Ma ◽  
Linfang Qian ◽  
Guangsong Chen
Keyword(s):  
Parameter Estimation ◽  
Contact Force ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Linear Method ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Force Model ◽  
Contact Force Model ◽  
Current Contact

Current contact force models are expected to be used under different environments, where the dynamical parameter estimation becomes an important issue in accurately analyzing the overall behavior of mechanical system especially for complex contact situations. In recent years, a significant amount of research has been carried out in relation to the nonlinear inverse problems, which can be generally divided into two categories: one is the linear method and the other can be called the nonlinear one. In this paper, both methods are described and compared. The linear method is based on the Taylor series and Exponentially Weighted Recursive Least Squares (EWRLS) estimation method. Whereas, the core of the nonlinear one is the Unscented Kalman Filter (UKF). The Lankarani–Nikravesh (L–N) contact force model is employed to quantify the contact effect in this paper, since it is proven to be more consistent with the physics of contact. Some simulation examples are employed to evaluate the convergence sensitivity of these two methods to parameter initial conditions. And the comparisons under the same simulation condition between both methods indicate that the nonlinear one is more robust and can converge faster than the linear one.

Parameter estimation for a chaotic dynamical system with partial observations

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jiantang Zhang ◽  
Sixun Huang ◽  
Jin Cheng
Keyword(s):  
Parameter Estimation ◽  
Initial Conditions ◽  
Original System ◽  
Data Preprocessing ◽  
Numerical Differentiation ◽  
Observation Data ◽  
Least Squares Regression ◽  
Lorenz Equations ◽  
Chaotic Dynamical System ◽  
Sensitive Dependence

Abstract Parameter estimation in chaotic dynamical systems is an important and practical issue. Nevertheless, the high-dimensionality and the sensitive dependence on initial conditions typically makes the problem difficult to solve. In this paper, we propose an innovative parameter estimation approach, utilizing numerical differentiation for observation data preprocessing. Given plenty of noisy observations on a portion of dependent variables, numerical differentiation allows them and their derivatives to be accurately approximated. Substituting those approximations into the original system can effectively simplify the parameter estimation problem. As an example, we consider parameter estimation in the well-known Lorenz model given partial noisy observations. According to the Lorenz equations, the estimated parameters can be simply given by least squares regression using the approximated functions provided by data preprocessing. Numerical examples show the effectiveness and accuracy of our method. We also prove the uniqueness and stability of the solution.

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