The innovation algorithms for multivariable state‐space models

Ding, Feng; Xiao, Zhang; Xu, Ling

doi:10.1002/acs.3053

Cited by 115 publications

(59 citation statements)

References 63 publications

(88 reference statements)

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“…There are still some interesting topics that are worth discussing. 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

Ding

Hayat

2020

Intl J Robust & Nonlinear

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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.

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Section: Discussionmentioning

confidence: 99%

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

Liu

Ding

Hayat

2020

Intl J Robust & Nonlinear

Self Cite

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“…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

Ding

et al. 2020

Intl J Robust & Nonlinear

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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.

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“…The estimation and prediction of climate changes are often based on mathematical models. Some of the predicted models can be established through certain parameter estimation methods [5][6][7][8], some use input-output representations [9][10][11], while others use state-space models [12] or network models [13,14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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The innovation algorithms for multivariable state‐space models

Cited by 115 publications

References 63 publications

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

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

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

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

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