Novel Method for Identifying Fault Location of Mixed Lines

Lin

et al. 2020

This article is concerned with the parameter identification of output-error bilinear-parameter models with colored noises from measurement data. An auxiliary model least squares-based iterative method is developed through the overparameterization model. It examines the difficulty of estimating the overparameterized vector, which usually presents a heavy computational burden in the identification process. To overcome this drawback, a parameter separation technique is introduced and the nonlinear model is reformulated as a refined identification model through eliminating the crossmultiplying terms. In this regard, a parameter separation least squares-based iterative (PS-LSI) algorithm is derived by avoiding estimating the redundant parameters. On the basis of the PS-LSI algorithm, we derive a maximum likelihood least squares-based iterative method to further improve the numerical accuracy. The identification is dependent on the formulation of a pseudolinear regression relationship, which contains two linear prefilters constructed from the system and noise models. The performance of this proposed method is confirmed by the numerical simulations as well as direct comparisons with other existing algorithms.

Section: Discussionmentioning

confidence: 99%

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

Chen

Lin

et al. 2020

“…The proposed methods proposed in this paper can be used for modeling and prediction [61][62][63][64][65] and can be extended to study the parameter estimation problems of different systems with colored noises [66][67][68][69][70] and can be applied to other literatures [71][72][73][74][75] such as information and networked communication systems. [76][77][78][79][80][81][82]…”

Section: Discussionmentioning

confidence: 99%

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

Liu

Hayat

2020

Summary The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.

“…The proposed methods in this paper can be extended to study the parameter estimation problems of bilinear systems with colored noises 12,62-64 and can be applied to other engineering literatures [65][66][67][68][69][70][71][72][73] such as signal modeling and communication networked systems. [74][75][76][77][78][79] In the future work, we will extend the method to study parameter estimation of the RBF-AR model with colored noises and study the estimation methods of model orders.…”

Section: Discussionmentioning

confidence: 99%

Recursive methods for estimating the radial basis function‐based state‐dependent autoregressive model

Zhou

et al. 2020

Summary Identifying a nonlinear radial basis function‐based state‐dependent autoregressive (RBF‐AR) time series model is the basis for solving the corresponding prediction and control problems. This paper studies some recursive parameter estimation algorithms for the RBF‐AR model. Considering the difficulty of the nonlinear optimal problem arising in estimating the RBF‐AR model, an overall forgetting gradient algorithm is deduced based on the negative gradient search. A numerical method with a forgetting factor is provided to solve the problem of determining the optimal convergence factor. In order to improve the parameter estimation accuracy, the multi‐innovation identification theory is applied to develop an overall multi‐innovation forgetting gradient (O‐MIFG) algorithm. The simulation results indicate that the estimation model based on the O‐MIFG algorithm can capture the dynamics of the RBF‐AR model very well.