Fitting Nonlinear Signal Models Using the Increasing-Data Criterion

Li, Jimei; Ding, Feng

doi:10.1109/lsp.2022.3177352

Cited by 30 publications

(14 citation statements)

References 32 publications

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“…The limitation of the methods is single-input single-output fractional-order nonlinear systems, we need to consider combining the hierarchical identification principle and the Kalman filtering technology to study identification problems of dual-rate systems, nonuniformly sampled-data systems, missing-data systems, multi-input multi-output systems and bilinear and nonlinear systems. The proposed recursive parameter, state and fractional-order identification for the fractional-order Hammerstein state-space systems by using the hierarchical identification principle and the Kalman filtering in this article can combine other identification methods [121][122][123][124][125][126][127][128] to investigate the identification problems of other linear nonlinear stochastic systems [129][130][131][132][133][134] and can be applied to other control and schedule areas [135][136][137][138][139] such as the information processing and transportation communication systems and so on.…”

Section: Discussionmentioning

confidence: 99%

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

2023

Adaptive Control & Signal

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Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.

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Section: Discussionmentioning

confidence: 99%

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

2023

Adaptive Control & Signal

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“…Notice that the parameters in the ECM, such as resistance and capacitance, are unknown and vary slightly as the battery is discharged. Therefore, recursive least squares algorithm with forgetting factor (FFRLS) is applied here for system identification purpose [21], [22].…”

Section: Fig 1: Thevenin Equivalent Circuit Diagrammentioning

confidence: 99%

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Shen

Ding

Hao

2023

Preprint

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This paper provides an insight into the estimation of state of charge (SOC) of lithium-ion batteries, and multi-measurement Kalman filters are constructed to achieve better estimation accuracy. The filter based estimation scheme consists of two sub Kalman filters, one based on the Thevenin equivalent circuit model and the other based on the Elman neural network model. They share the same state equation but have different measurements, namely from the terminal voltage and the Elman neural network, respectively. The optimal weighted error covariance matrix in Kalman filter is computed to obtain the final estimated SOC with the results from two sub filters. The training set of the experiment is from five mixed charging and discharging cycles and the test set is the LA92 driving cycle. The experimental results show that multi-measurement Kalman filtering has higher accuracy in SOC estimation, both the mean absolute error and the root mean square error are less than 0.3\%.

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“…The two-stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation in this article can integrate some filtering and estimation approaches [89][90][91][92][93][94] to explore identification methods of dynamical stochastic linear and nonlinear systems [95][96][97][98][99][100] and can be applied to engineering fields [101][102][103][104][105][106] such as the information processing systems. The KT-AM-2S-RG algorithm involves the following steps:…”

Section: Kt-am-2s-rg Algorithmmentioning

confidence: 99%

Two‐stage and three‐stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation

Lv,

Sun,

Pan

2023

Intl J Robust & Nonlinear

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This article explores recursive algorithms for parameter identification issues of Hammerstein output‐error systems. The proposed approach includes the key term separation auxiliary model recursive gradient algorithm, which utilizes the gradient search and the key term separation. To enhance computational efficiency, the system is decomposed into two or three subsystems through the hierarchical identification principle. Based on this, a key term separation based auxiliary model two‐stage recursive gradient algorithm and a key term separation based auxiliary model three‐stage recursive gradient algorithm are presented. The simulation results verify the validity of the obtained algorithms.

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Fitting Nonlinear Signal Models Using the Increasing-Data Criterion

Cited by 30 publications

References 32 publications

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Two‐stage and three‐stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation

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