Partially‐coupled least squares based iterative parameter estimation for multi‐variable output‐error‐like autoregressive moving average systems

Ma, Hao; Pan, Jian; Ding, Feng; Xu, Ling; Ding, Wenfang

doi:10.1049/iet-cta.2019.0112

Cited by 164 publications

(96 citation statements)

References 57 publications

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“…Therefore, we used the ARIMA model to model and predict the trend component, and obtained more accurate results than neural network GRU. The proposed methods proposed in this paper can combine other identification approaches [38][39][40][41][42] to study the modeling and prediction problems of other dynamic time series and stochastic systems with colored noises [43][44][45][46][47], and can be applied to other fields [48][49][50][51][52] such as signal modeling and control systems [53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

et al. 2019

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Featured Application: This work can be used in the intelligent system for the smart city, smart agriculture, etc.Abstract: It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for long-term prediction. Appl. Sci. 2019, 9, 4533 2 of 14 models [14] and vector auto-regression (VAR) [15]. ARIMA is a linear modeling method that provides accurate predictions for approximate linear relationships. However, its prediction performance is not good enough for nonlinear prediction problems.To capture the complex nonlinearity of PM2.5, a back propagation (BP) neural network has been widely applied in time-series data prediction [16][17][18]. Other shallow nonlinear networks based on a machine-learning mechanism such as the improved gray neural network model [19] and the radial basis function (RBF) neural network [20] have also been used to predict time-series data. Xu et al. [3] proposed a supplementary leaky integrator echo state network (SLI-ESN), which added the historical state term of the historical moment to the calculation of a leaky integrator reservoir. Compared with an echo state network (ESN), a leaky integrator ESN (LI-ESN), an extreme learning machine (ELM), a hierarchical ELM (H-ELM), a stacked auto-encoder (SAE), and a traditional SLI-ESN, the proposed SLI-ESN of Xu et al.[3] could achieve good prediction results, but its long-term predictions were not satisfactory.As a shallow network still cannot effectively extract the complex nonlinearity of the data, decomposition must be used; that is, the data must be decomposed into multiple components to reduce its complexity, and then multiple sub-models are used to improve the prediction performance. For example, García et al. [21] decomposed a long-term data series into smaller seasonal component patterns. Jesús et al. [22] divided a pollen-concentration data series into seasonal and random parts, used partial least-squares regression (PLSR) to fit the residuals, and established an airborne pollen time-series model to predict the daily pollen concentration. Ming et al.[23] extracted accurate seasonal signals, a...

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Section: Discussionmentioning

confidence: 99%

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

et al. 2019

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“…10,11 Parameter estimation and state filtering are basic for system control and system analysis. 12,13 Many parameter estimation methods, such as hierarchical identification methods, 14,15 Newton identification methods, [16][17][18] and coupled identification methods, 19,20 have been widely studied. In the literature, Waschburger and Galvão investigated a method to estimate the input delays of a discrete-time state-space model by utilizing the standard least squares methods to minimize a quadratic cost function of the prediction error of the system states within a given time range.…”

Section: Introductionmentioning

confidence: 99%

Adaptive parameter estimation for a general dynamical system with unknown states

Xiao

Ding

2020

Intl J Robust & Nonlinear

Self Cite

116

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Summary This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms.

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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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Partially‐coupled least squares based iterative parameter estimation for multi‐variable output‐error‐like autoregressive moving average systems

Cited by 164 publications

References 57 publications

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

Adaptive parameter estimation for a general dynamical system with unknown states

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

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