A WSN-based prediction model of microclimate in a greenhouse using extreme learning approaches

Liu, Q, Jin, D, Shen, J, Fu, Z and Linge, N 2016, 'A WSN-based prediction model of microclimate in a greenhouse using extreme learning approaches' , in: 2016 18th International Conference on Advanced Communication Technology (ICACT) , IEEE, pp. 730-735.

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Abstract

Monitoring and controlling microclimate in a greenhouse becomes one of the research hotspots in the field of agrometeorology, where the application of Wireless Sensor Networks (WSN) recently attracts more attentions due to its features of self-adaption, resilience and cost-effectiveness. Present microclimate monitoring and control systems achieve their prediction by manipulating captured environmental factors and traditional neural network algorithms; however, these systems suffer the challenges of quick prediction (e.g. hourly and even minutely) when a WSN network is deployed. In this paper, a novel prediction method based on an Extreme Learning Machine (ELM) algorithm and KELM (Kernel based ELM) is proposed to predict the temperature and humidity in a practical greenhouse environment in Nanjing, China. Indoor temperature and humidity are measured as data samples via WSN nodes. According to the results, our approach (0.0222s) has shown significant improvement on the training speed than Back Propagation (Bp) (0.7469s), Elman (11.3307s) and Support Vector Machine (SVM) (19.2232s) models, the accuracy rate of our model is higher than those models. In the future, research on faster learning speed of the ELM and KELM based neural network model will be conducted.

Item Type: Book Section
Schools: Schools > School of Computing, Science and Engineering
Publisher: IEEE
ISBN: 9788996865063
ISSN: 1738-9445
Depositing User: WM Taylor
Date Deposited: 15 Dec 2016 11:16
Last Modified: 15 Dec 2016 11:16
URI: http://usir.salford.ac.uk/id/eprint/41012

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