IEICE TRANSACTIONS on Information
A Novel Lambertian-RBFNN for Office Light Modeling
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.7 pp.1742-1752
- Publication Date
- 2016/07/01
- Publicized
- 2016/04/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDP7411
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Wa SI
Waseda University
Xun PAN
Waseda University
Harutoshi OGAI
Waseda University
Katsumi HIRAI
Hakutsu Technology Ltd.
Keyword
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Wa SI, Xun PAN, Harutoshi OGAI, Katsumi HIRAI, "A Novel Lambertian-RBFNN for Office Light Modeling" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 7, pp. 1742-1752, July 2016, doi: 10.1587/transinf.2015EDP7411.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7411/_p
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@ARTICLE{e99-d_7_1742,
author={Wa SI, Xun PAN, Harutoshi OGAI, Katsumi HIRAI, },
journal={IEICE TRANSACTIONS on Information},
title={A Novel Lambertian-RBFNN for Office Light Modeling},
year={2016},
volume={E99-D},
number={7},
pages={1742-1752},
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.},
keywords={},
doi={10.1587/transinf.2015EDP7411},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - A Novel Lambertian-RBFNN for Office Light Modeling
T2 - IEICE TRANSACTIONS on Information
SP - 1742
EP - 1752
AU - Wa SI
AU - Xun PAN
AU - Harutoshi OGAI
AU - Katsumi HIRAI
PY - 2016
DO - 10.1587/transinf.2015EDP7411
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2016
AB - 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.
ER -
English
