An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes

C. P. Kurian, S. Kuriachan, J. Bhat, R. S. Aithal

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Advanced lighting simulation tools as well as computationally intelligent systems present the possibility of using a model based on computation as a means of controlling lighting on the visual task. Lighting control has now become an essential element of good design and an integral part of energy management programmes. This paper presents a novel computational model suitable for the adaptive predictive control of artificial light in accordance with the variation of daylight. Simulated data and an adaptive neuro-fuzzy inference system are incorporated into the model. The software package Radiance is used to carry out the simulation. In this process, the role of a simulator is considered as the source of the system knowledge by which a supervised learner, implemented in adaptive neuro-fuzzy inference system is trained for faster predictions. The goal of this paper is to make use of the benefits of the hybridization between simulation and machine learning for the purpose of light control.

Original languageEnglish
Pages (from-to)343-352
Number of pages10
JournalLighting Research and Technology
Volume37
Issue number4
DOIs
Publication statusPublished - 2005

Fingerprint

Lighting
Fuzzy inference
Energy management
Intelligent systems
Software packages
Learning systems
Simulators

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

@article{cb9ea89d4f7b4f2197739dc07d3748d5,
title = "An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes",
abstract = "Advanced lighting simulation tools as well as computationally intelligent systems present the possibility of using a model based on computation as a means of controlling lighting on the visual task. Lighting control has now become an essential element of good design and an integral part of energy management programmes. This paper presents a novel computational model suitable for the adaptive predictive control of artificial light in accordance with the variation of daylight. Simulated data and an adaptive neuro-fuzzy inference system are incorporated into the model. The software package Radiance is used to carry out the simulation. In this process, the role of a simulator is considered as the source of the system knowledge by which a supervised learner, implemented in adaptive neuro-fuzzy inference system is trained for faster predictions. The goal of this paper is to make use of the benefits of the hybridization between simulation and machine learning for the purpose of light control.",
author = "Kurian, {C. P.} and S. Kuriachan and J. Bhat and Aithal, {R. S.}",
year = "2005",
doi = "10.1191/1365782805li150oa",
language = "English",
volume = "37",
pages = "343--352",
journal = "Lighting Research and Technology",
issn = "1477-1535",
publisher = "SAGE Publications Ltd",
number = "4",

}

An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes. / Kurian, C. P.; Kuriachan, S.; Bhat, J.; Aithal, R. S.

In: Lighting Research and Technology, Vol. 37, No. 4, 2005, p. 343-352.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes

AU - Kurian, C. P.

AU - Kuriachan, S.

AU - Bhat, J.

AU - Aithal, R. S.

PY - 2005

Y1 - 2005

N2 - Advanced lighting simulation tools as well as computationally intelligent systems present the possibility of using a model based on computation as a means of controlling lighting on the visual task. Lighting control has now become an essential element of good design and an integral part of energy management programmes. This paper presents a novel computational model suitable for the adaptive predictive control of artificial light in accordance with the variation of daylight. Simulated data and an adaptive neuro-fuzzy inference system are incorporated into the model. The software package Radiance is used to carry out the simulation. In this process, the role of a simulator is considered as the source of the system knowledge by which a supervised learner, implemented in adaptive neuro-fuzzy inference system is trained for faster predictions. The goal of this paper is to make use of the benefits of the hybridization between simulation and machine learning for the purpose of light control.

AB - Advanced lighting simulation tools as well as computationally intelligent systems present the possibility of using a model based on computation as a means of controlling lighting on the visual task. Lighting control has now become an essential element of good design and an integral part of energy management programmes. This paper presents a novel computational model suitable for the adaptive predictive control of artificial light in accordance with the variation of daylight. Simulated data and an adaptive neuro-fuzzy inference system are incorporated into the model. The software package Radiance is used to carry out the simulation. In this process, the role of a simulator is considered as the source of the system knowledge by which a supervised learner, implemented in adaptive neuro-fuzzy inference system is trained for faster predictions. The goal of this paper is to make use of the benefits of the hybridization between simulation and machine learning for the purpose of light control.

UR - http://www.scopus.com/inward/record.url?scp=29044443874&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=29044443874&partnerID=8YFLogxK

U2 - 10.1191/1365782805li150oa

DO - 10.1191/1365782805li150oa

M3 - Article

AN - SCOPUS:29044443874

VL - 37

SP - 343

EP - 352

JO - Lighting Research and Technology

JF - Lighting Research and Technology

SN - 1477-1535

IS - 4

ER -