Name
Title | ||
|---|---|---|
Greenhouse Indoor Temperature Prediction Based on Extreme Learning Machines for Resource-Constrained Control Devices Implementation |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-19917-2_25 | HIGHLIGHTS IN PRACTICAL APPLICATIONS OF AGENTS AND MULTIAGENT SYSTEMS |
Field | DocType | Volume |
Embedded controller,Simulation,Extreme learning machine,Computer science,Real-time computing,Greenhouse | Conference | 89 |
ISSN | Citations | PageRank |
1867-5662 | 0 | 0.34 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Paniagua-Tineo | 1 | 9 | 2.07 |
| Sancho Salcedo-Sanz | 2 | 580 | 71.21 |
| Emilio G. Ortíz-García | 3 | 40 | 6.08 |
| Antonio Portilla-Figueras | 4 | 147 | 19.07 |
| B. Saavedra-Moreno | 5 | 18 | 2.87 |
| G. López-Díaz | 6 | 0 | 0.34 |