Abstract
Due to the difficulty of establishing the accurate control model for building an intelligent system, a neural network predictive control method is proposed, in this paper, based on a weed optimization algorithm. Through considering indoor temperature and relative humidity environment factors, a control model of temperature and humidity environment is first established in an intelligent building. Then, the hidden layer nodes center of the RBF neural network is optimized by using the weed optimization algorithm. The above mentioned work focuses on improving the shortcomings of Orthogonal Least Squares (OLS) algorithm, and simultaneously simplifies the network architecture. The simulation results show that the RBF neural network predictive control method based on the weed optimization algorithm has better approximation ability and generalization ability contrasting with the OLS algorithm.
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Notes
- 1.
The simulation study of this research work has been conducted with approval obtained from the owner of the residential in Xiangfenghuayuan, Huanggu District, Shenyang, China.
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Acknowledgements
This research work is partially supported by the National Natural Youth Science Foundation of China (Project Codes: 61305125), Shenyang Jianzhu University Discipline Content Education Project (Project Codes: XKHY2-66), the Natural Science Foundation of University (Project Codes: 2014068) and National Post Doctor Foundation (Project Codes: 2013M530955, 2014T70265).
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Huang, K., Song, H., Fu, H. (2019). Study on the Control Method of Temperature and Humidity Environment in Building Intelligent System. In: Fang, Q., Zhu, Q., Qiao, F. (eds) Advancements in Smart City and Intelligent Building. ICSCIB 2018. Advances in Intelligent Systems and Computing, vol 890 . Springer, Singapore. https://doi.org/10.1007/978-981-13-6733-5_1
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DOI: https://doi.org/10.1007/978-981-13-6733-5_1
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