Parameter Estimation for Nonlinear Systems with Multirate Measurements and Random Delays

AIChE Journal ◽  
2021 ◽  
Author(s):  
Ruijing Han ◽  
Yousef Salehi ◽  
Biao Huang ◽  
Vinay Prasad
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Random Delays

On the accuracy of parameter estimation for continuous time nonlinear systems from sampled data

2011 ◽  
Author(s):  
Diego S. Carrasco ◽  
Lennart Ljung ◽  
Graham C. Goodwin ◽  
Juan C. Aguero
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Continuous Time ◽  
Sampled Data

PSO-based Parameter Estimation of Nonlinear Systems

2006 ◽  
Author(s):  
Ye Meiying ◽  
Wang Xiaodong
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems

A New Integrated Algorithm of Structure Determination and Parameter Estimation for Nonlinear Systems Identification

1999 ◽  
Author(s):  
Wang Xiao Wang ◽  
Jianyin Xie
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Structure Determination ◽  
Model Identification ◽  
Identification Algorithm ◽  
Householder Transformation ◽  
Systems Identification ◽  
Value Decomposition ◽  
Svd Method ◽  
Nonlinear Systems Identification

Abstract A new integrated algorithm of structure determination and parameter estimation is proposed for nonlinear systems identification in this paper, which is based on the Householder Transformation (HT), Givens and Modified Gram-Schmidt (MGS) algorithms. While being used for the polynomial and rational NARMAX model identification, it can select the model terms while deleting the unimportant ones from the assumed full model, avoiding the storage difficulty as the CGS identification algorithm does which is proposed by Billings et. al., and is numerically more stable. Combining the H algorithm with the modified bidiagonalization least squares (MBLS) algorithm and the singular value decomposition (SVD) method respectively, two algorithms referred to as the MBLSHT and SVDHT ones are proposed for the polynomial and rational NARMAX model identification. They are all numerically more stable than the HT or Givens or MGS algorithm given in this paper, and the MBLSHT algorithm has the best performance. A higher precision for the parameter estimation can thus be obtained by them, as supported b simulation results.

A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1950075 ◽  
Author(s):  
Gong Ren ◽  
Renhuan Yang ◽  
Renyu Yang ◽  
Pei Zhang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Engineering System ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
System Characteristics

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

Parameter Estimation in Nonlinear Systems with Auto and Crosscorrelated Noise

2000 ◽  
Vol 33 (15) ◽  
pp. 1079-1084
Author(s):  
B. David ◽  
G. Bastin
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems

Parameter estimation methodology for nonlinear systems: Application to induction motor

2005 ◽  
Vol 14 (2) ◽  
pp. 240-254 ◽  
Author(s):  
G. Kenne ◽  
F. Floret ◽  
H. Nkwawo ◽  
F. Lamnabhi-Lagarrigue
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Induction Motor

scholarly journals A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shaolong Chen ◽  
Renyu Yang ◽  
Renhuan Yang ◽  
Liu Yang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
System Modeling ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Function Parameter ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

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