Journal ArticleDOI
The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise
TLDR
This paper gives the input-output representation of a bilinear system through eliminating the state variables in it, and derives a maximum likelihood least squares based iterative for identifying the parameters of bil inear systems with colored noises by using the maximum likelihood principle.Abstract:
Maximum likelihood methods are significant for parameter estimation and system modeling. This paper gives the input-output representation of a bilinear system through eliminating the state variables in it, and derives a maximum likelihood least squares based iterative for identifying the parameters of bilinear systems with colored noises by using the maximum likelihood principle. A least squares based iterative (LSI) algorithm is presented for comparison. It is proved that the maximum of the likelihood function is equivalent to minimize the least squares cost function. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems and the maximum likelihood LSI algorithm is more accurate than the LSI algorithm.read more
Citations
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Journal ArticleDOI
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
TL;DR: In this paper, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition, and the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm.
Journal ArticleDOI
The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique
Meihang Li,Ximei Liu +1 more
TL;DR: A two-stage least squares based iterative algorithm and a filtering based least squares iterative algorithms are proposed for estimating the parameters of bilinear systems with colored noises by using the hierarchical identification principle and the data filtering technique.
Journal ArticleDOI
The parameter estimation algorithms based on the dynamical response measurement data
TL;DR: In this article, the authors studied the parameter estimation to the system response from the discrete measurement data, by constructing the dynamical rolling cost functions and using the nonlinear optimization, t
Journal ArticleDOI
Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique
Meihang Li,Ximei Liu +1 more
TL;DR: In this paper, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter.
Journal ArticleDOI
A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation
TL;DR: This paper considers the parameter identification for Hammerstein controlled autoregressive systems by using the key term separation technique to express the system output as a linear combination of the system parameters, and then a hierarchical least squares algorithm is developed for estimating all parameters involving in the subsystems.
References
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The damping iterative parameter identification method for dynamical systems based on the sine signal measurement
TL;DR: A damping parameter estimation algorithm for dynamical systems based on the sine frequency response is proposed and a damping factor is introduced in the proposed iterative algorithm in order to overcome the singular or ill-conditioned matrix during the iterative process.
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Ling Xu,Lei Chen,Weili Xiong +2 more
TL;DR: In this article, a new Newton iterative identification method is presented for estimating the parameters of a second-order dynamic system utilizing the obtained data from the step response, in order to obtain the desired dynamic performance, a controller design method based on the root locus is presented to meet the requirement of the dynamic performance of the overshoot.
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Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model
Yanjiao Wang,Feng Ding +1 more
TL;DR: For a MIMO system whose outputs are contaminated by an ARMA noise process, an auxiliary model based recursive least squares parameter estimation algorithm is presented through filtering input-output data, which has higher estimation accuracy than the existing multivariable identification algorithm.
Journal ArticleDOI
Stabilization for a Class of Switched Nonlinear Systems With Novel Average Dwell Time Switching by T–S Fuzzy Modeling
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Application of the Newton iteration algorithm to the parameter estimation for dynamical systems
TL;DR: Simulation results show that the obtained models can capture the dynamics of the systems, i.e., the estimated model's outputs are close to the outputs of the actual systems.