Name
Abstract | ||
|---|---|---|
In lighting control systems, accurate data of artificial light (lighting coefficients) are essential for the illumination control accuracy and energy saving efficiency. This research proposes a novel Lambertian-Radial Basis Function Neural Network (L-RBFNN) to realize modeling of both lighting coefficients and the illumination environment for an office. By adding a Lambertian neuron to represent the rough theoretical illuminance distribution of the lamp and modifying RBF neurons to regulate the distribution shape, L-RBFNN successfully solves the instability problem of conventional RBFNN and achieves higher modeling accuracy. Simulations of both single-light modeling and multiple-light modeling are made and compared with other methods such as Lambertian function, cubic spline interpolation and conventional RBFNN. The results prove that: 1) L-RBFNN is a successful modeling method for artificial light with imperceptible modeling error; 2) Compared with other existing methods, L-RBFNN can provide better performance with lower modeling error; 3) The number of training sensors can be reduced to be the same with the number of lamps, thus making the modeling method easier to apply in real-world lighting systems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1587/transinf.2015EDP7411 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
illumination modeling, RBFNN, lambertian function | Computer vision,Pattern recognition,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E99D | 7 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wa Si | 1 | 4 | 2.46 |
| Xun Pan | 2 | 4 | 2.46 |
| Harutoshi Ogai | 3 | 13 | 9.93 |
| Katsumi Hirai | 4 | 5 | 4.55 |