Modelling of colour perception of different age groups using artificial neural networks

被引:28
作者
Kose, Erdogan [1 ]
机构
[1] Gazi Univ, Tech Educ Fac, Press Dept, TR-06500 Ankara, Turkey
关键词
colour; colour perception; artificial neural network;
D O I
10.1016/j.eswa.2007.02.036
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Colour is a subjective term which changes according to each person's view. We should define colour physically to work with it in terms of printing or any other professional study. However, enabling the observation of colour in terms of sales and marketing and product design and development is as important as product and its packaging. The perception of colour and tendencies to colours will be different for people of different age groups and socio-economical levels. In this study, different colour samples will be shown to four different groups of primary school, university, midage and elderly people and to two different categories, namely men and women. The aim of the study is to determine which colour is more perceived and adopted by which group and which category using artificial neural network (ANN). In the direction of these determinations it has been studied with graphical and statistical illustrations how different age groups prefer colours, how much they are able to recognise primary and secondary colours and to what extent they are able to perceive them correctly. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2129 / 2139
页数:11
相关论文
共 23 条
[1]  
*AGF GEV, 1997, COL TUN 3 0 MAC US G
[2]  
[Anonymous], ENERGY CONVERS MANAG
[3]   1ST-ORDER AND 2ND-ORDER METHODS FOR LEARNING - BETWEEN STEEPEST DESCENT AND NEWTON METHOD [J].
BATTITI, R .
NEURAL COMPUTATION, 1992, 4 (02) :141-166
[4]   New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks [J].
Bechtler, H ;
Browne, MW ;
Bansal, PK ;
Kecman, V .
APPLIED THERMAL ENGINEERING, 2001, 21 (09) :941-953
[5]   Modeling of thermodynamic properties using neural networks - Application to refrigerants [J].
Chouai, A ;
Laugier, S ;
Richon, D .
FLUID PHASE EQUILIBRIA, 2002, 199 (1-2) :53-62
[6]  
*COL COL MAN, 1999, TECHN B
[7]   TRAINING FEEDFORWARD NETWORKS WITH THE MARQUARDT ALGORITHM [J].
HAGAN, MT ;
MENHAJ, MB .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (06) :989-993
[8]  
Haykin S., 1998, NEURAL NETWORKS COMP
[9]  
*IFRA, 1999, COL ICC OUTP PROF PR
[10]   Artificial intelligence for the modeling and control of combustion processes: a review [J].
Kalogirou, SA .
PROGRESS IN ENERGY AND COMBUSTION SCIENCE, 2003, 29 (06) :515-566