دورية أكاديمية
Atmospheric PM concentration prediction and noise estimation based on adaptive unscented Kalman filtering
| العنوان: | Atmospheric PM concentration prediction and noise estimation based on adaptive unscented Kalman filtering |
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| المؤلفون: | Jihan Li, Xiaoli Li, Kang Wang, Guimei Cui |
| المصدر: | Measurement + Control, Vol 54 (2021) |
| بيانات النشر: | SAGE Publishing, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Technology (General) |
| مصطلحات موضوعية: | Control engineering systems. Automatic machinery (General), TJ212-225, Technology (General), T1-995 |
| الوصف: | Due to the randomness and uncertainty in the atmospheric environment, and accompanied by a variety of unknown noise. Accurate prediction of PM 2.5 concentration is very important for people to prevent injury effectively. In order to predict PM 2.5 concentration more accurately in this environment, a hybrid modelling method of support vector regression and adaptive unscented Kalman filter (SVR-AUKF) is proposed to predict atmospheric PM 2.5 concentration in the case of incorrect or unknown noise. Firstly, the PM 2.5 concentration prediction model was established by support vector regression. Secondly, the state space framework of the model is combined with the adaptive unscented Kalman filter method to estimate the uncertain PM 2.5 concentration state and noise through continuous updating when the model noise is incorrect or unknown. Finally, the proposed method is compared with SVR-UKF method, the simulation results show that the proposed method is more accurate and robust. The proposed method is compared with SVR-UKF, AR-Kalman, AR and BP methods. The simulation results show that the proposed method has higher prediction accuracy of PM 2.5 concentration. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 0020-2940 00202940 |
| العلاقة: | https://doaj.org/toc/0020-2940Test |
| DOI: | 10.1177/0020294021997491 |
| الوصول الحر: | https://doaj.org/article/2f831dba9ba8407aa901f8376db157adTest |
| رقم الانضمام: | edsdoj.2f831dba9ba8407aa901f8376db157ad |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 00202940 |
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| DOI: | 10.1177/0020294021997491 |