A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai, Yuting; Wang, Xiaoyi; Jin, Xue-Bo; Zhao, Zhiyao; Zhang, Baihai

doi:10.3390/s20010299

Cited by 64 publications

(39 citation statements)

References 72 publications

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“…For medium-term prediction, S t takes the other three components and we set n as 24. This means we used the data from the historical 24 h to predict the data of the future 24 h. The method proposed in this paper can be combined with other system identification methods [30][31][32] to study the modeling and prediction of other dynamic time series and random systems [33,34] and can be applied to other fields [35][36][37] and other signal modeling and control systems [6,[38][39][40].…”

Section: Long-term Predictionmentioning

confidence: 99%

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Jin

Wang

et al. 2020

Sustainability

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Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

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Section: Long-term Predictionmentioning

confidence: 99%

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Jin

Wang

et al. 2020

Sustainability

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“…Moreover, the prediction of the water quality [39] should be introduced to pre-judge the trend. The prediction models [40][41][42][43] and data estimation methods [44,45] can help data analysis in the aforehand decision-making.…”

Section: Extension and Improvement Of Group Decision-makingmentioning

confidence: 99%

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

et al. 2020

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Algal bloom is a typical pollution of urban lakes, which threatens drinking safety and breaks the urban landscape. It is pivotal to select a reasonable governance approach for sustainable management. A decision-making support method was studied in this paper. First, a general framework was designed to organize the rational decision-making processes. Second, quantitative calculation methods were proposed, including expert selection and opinion integration. The methods can determine the vital decision elements objectively and automatically. Third, the method was applied in Yuyuantan Lake in Beijing, China. The monitoring information and decision-making process are presented and the rank of governance alternatives is given. The comparison and discussion show that the group decision-making method is feasible and effective. It can assist the sustainable management of algal bloom.

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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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A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Cited by 64 publications

References 72 publications

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

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

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