Abstract
In this paper we present an Extreme Learning Machine approach for a real problem of indoor temperature prediction in greenhouses. In this specific problem, the computational cost of the forecasting algorithm is capital, since it should be implemented in resource-constrained devices, typically an embedded controller. We show that the ELM algorithm is extremely fast, and obtains a reasonable performance in this problem, so it is a very good option for a real implementation of the temperature forecasting system in greenhouses.
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Paniagua-Tineo, A., Salcedo-Sanz, S., Ortiz-García, E.G., Portilla-Figueras, A., Saavedra-Moreno, B., López-Díaz, G. (2011). Greenhouse Indoor Temperature Prediction Based on Extreme Learning Machines for Resource-Constrained Control Devices Implementation. In: Pérez, J.B., et al. Highlights in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol 89. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19917-2_25
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DOI: https://doi.org/10.1007/978-3-642-19917-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19916-5
Online ISBN: 978-3-642-19917-2
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