Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements

Li, Junhong; Zheng, Wei Xing; Gu, Juping; Hua, Liu

doi:10.1016/j.jfranklin.2016.10.002

Cited by 75 publications

(25 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation results show that the proposed algorithms can generate accurate estimates. The proposed approaches in the paper can combine other mathematical tools [64][65][66][67][68][69] and statistical strategies [70][71][72][73][74][75] to study the performances of some parameter estimation algorithms and can be applied to other multivariable systems with different structures and disturbance noises and other literature [76][77][78][79][80][81][82][83][84][85][86] such as system identification [87][88][89][90][91][92].…”

Section: Discussionmentioning

confidence: 99%

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

View full text Add to dashboard Cite

The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input-output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

View full text Add to dashboard Cite

show abstract

“…The LM algorithm can be seen as a combination of the steepest gradient algorithm and the Newton algorithm (Li et al, 2017), providing point estimates. Short cadence data from Quarter 1 were downloaded for Kepler-1b, 5b, and 8b from the NEA website and folded using the given (NEA) system periods.…”

Section: Levenberg-marquadt (Lm) Fitsmentioning

confidence: 99%

Transit modelling of selected Kepler systems

Huang¹,

Banks

Budding

et al. 2019

Astrophys Space Sci

View full text Add to dashboard Cite

This paper employs a simple model, considering just geometry and linear or quadratic limb darkening, to fit Kepler transit data via a Markov Chain Monte Carlo (MCMC) methodology for Kepler-1b, 5b, 8b, 12b, 77b, 428b, 491b, 699b, 706b, and 730b. Additional fits were made of the systems using the more sophisticated modeller Winfitter, which gives results in general agreement with the simpler model. Analysis of data with longer integration times showed biasing of the derived parameters, as expected from the literature, leading to larger estimates for radii and reducing estimates of the system inclination.

show abstract

“…The prediction oscillation is more obvious and has a greater impact on the prediction accuracy and stability. So the LM algorithm [24,25] was adopted to replace the gradient descent algorithm to optimize the weighting mechanism of the CDBN model, the information extraction stability of the model and the generalization ability of the nonlinear problem of the time-varying system are improved, the speed of the model convergence is accelerated, and the prediction accuracy and stability of the model are improved. Considering the complexity of the deformation of supertall buildings, the complexity of deformation factors, the deformation characteristics, and the advantages and disadvantages of the model, the LM-CDBN model was applied to the deformation prediction of the CITIC tower, and the prediction accuracy and stability of the proposed model are verified by model comparison experiments and predictive analysis.…”

Section: Introductionmentioning

confidence: 99%

A Deformation Prediction Approach for Supertall Building Using Sensor Monitoring System

Qiu

Wang

et al. 2019

Journal of Sensors

View full text Add to dashboard Cite

Using high-precision sensors to monitor and predict the deformation trend of supertall buildings is a hot research topic for a long time. And in terms of deformation trend prediction, the main way to realized deformation trend prediction is the deep learning algorithm, but the accuracy of prediction result needs to be improved. To solve the problem described above, firstly, based on the conditional deep belief network (CDBN) model, the levenberg-marquardt (LM) was used to optimize the CDBN model; the LM-CDBN model has been constructed. Then taking CITIC tower, the tallest building in Beijing as the research object, the real-time monitoring data of the shape acceleration array (SAA) as an example, we used LM-CDBN model to analyse and predict the building deformation. Finally, to verify the accuracy and robustness of LM-CDBN model, the prediction results of the LM-CDBN model are compared with the prediction results of the CDBN model, the extreme learning machine (ELM) model, and the unscented Kalman filter-support vector regression (UKF-SVR) model, and we evaluated the result from three aspects: training error, fitness, and stability of prediction results. The results show that the LM-CDBN model has higher precision and fitting degree in the prediction of deformation trend of supertall buildings. And the MRE, MAE, and RMSE of the LM-CDBN model prediction results are only 0.0060, 0.0023mm, and 0.0031mm, and the prediction result was more in line with the actual deformation trend.

show abstract

Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements

Cited by 75 publications

References 34 publications

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Transit modelling of selected Kepler systems

A Deformation Prediction Approach for Supertall Building Using Sensor Monitoring System

Contact Info

Product

Resources

About