基于BP神经网络的春夏女装流行色预测研究
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摘要
随着色彩经济的盛行,流行色在纺织服装行业产品中的附加值越来越为企业所重视,色彩预测工作已经成为纺织服装行业中必不可少的环节。我国对流行色的研究起步晚,色彩理论研究大多来源于国外,服装流行色发布仍是以国外权威机构的发布为重要参考,国内企业对诸如国际流行色协会、美国棉花公司、潘通公司等发布的流行色信息具有很大的依赖性。如今,越来越多的国内机构在参考国外流行色发布的基础上,结合国内的市场行情,进行综合预测。然而,在计算机技术高度发达的时代,现有的国内外预测方法在实际可行、可达到性以及对流行色的非线性变化关系的处理上还存有缺陷。
本课题对国内外有关流行色属性规律分析基础理论、色彩技术、预测及应用方面的研究文献进行了查阅和综述,概述流行色及流行色预测有关概念,从主客观的角度探讨了流行色变化规律,分析流行色预测的影响因素及预测思想方法的优缺点,为流行色的量化预测做准备。基于BP神经网络通过人工智能的方法能够逼近任意关系函数,并且可以根据需要进行自主程序设计,输出更接近最终流行色预测结果的数据,课题对这一预测方法的科学性、全面性、连续性进行了探索。首先搜集权威机构2001—2011年发布的春夏女装流行色色卡,采用科学的CIELab色度测量方法对其进行量化,并将色彩的属性特征数据进行科学归类,建立BP神经网络的样本学习数据库和训练模型,进行流行色色相、色调、单个色相的色调预测与分析验证。
课题将流行色特征通过色卡和量化数据图表两种形式形象地、清晰地表达出来,色卡样本分项统计和图表分析结果直观地显示,春夏女装流行色属性存在不变、渐变与突变的三重特征,这为量化流行色预测提供了依据。研究揭示流行色预测既有色彩量化、流行色规律等有利条件的支撑,也受到流行色突变性变化、客观限制性、主观不确定性等不利因素的影响。为了最大程度准确预测流行色定案中的关键参数,课题进行2011年春夏流行色色相、色调频数以及红色调L*、a*、b*对应的min、max值估计下色调分布的BP神经网络预测与检验以及2012年再预测,2011年各项预测结果与实际流行趋势发布基本吻合,表明该方法能科学、全面、连续地预测流行色,因此本课题研究丰富和发展了流行色预测理论。
With the prevailing of color economy, more and more attention has been paid on the added value brought by fashion color in textile and garment industry, color forecasting has become an indispensable link. Most of the domestic color theories are from abroad because of the late start of China's apparel industry, foreign authorities releases play an important role as reference and domestic enterprises are dependent on the color information released by such institutions as International Fashion Color Association, Cotton Incorporated, Pantone Company and so on. Today, based on the combination of domestic market conditions, more and more domestic institutions can do a comprehensive prediction job in reference to foreign release. However, in the era of the highly developed computer technology, the existing prediction methods at home and abroad still reveal flaws, such as practicality, reachability and the problems of handling the nonlinear variation of fashion color.
This subject searches and summarizes the domestic and international relevant literatures on basic theories of fashion color characteristic rules, color technology, forecast and application in order to have an overview of fashion color and its forecasts, and then explore the changing rules from the perspective of subjective and objective, analyzes the advantages and disadvantages of the forecasting thoughts and impacting forecasting factors for the preparation of quantitative forecast. As the BP neural network can approach any relationship function through artificial intelligence methods, complete self-programming design according to the need and then output data that are closer to the final result, this subject discusses the scientific, comprehensive, continuous characters of this method. Firstly, Women's SS fashion color cards from 2001 to 2011 released by authoritative organ are collected and quantified with a scientific method of CIELab color measurement, then, the color characteristic data are classified as the object of experimental study, at last, fashion color hue , tone and the tone of single color are forecasted and estimated by the establishment of sample learning database and training model of the BP neural network.
This subject clearly illustrate the vivid fashion color characteristics via color cards and quantitative data charts, which shows that there is triple features of women's SS fashion color, constant, gradation and mutation, this provides foundation for quantitative fashion color forecast. Also fashion color forecast not only has a support of favorable conditions like color quantization, fashion color rules, but also received negative influences like abrupt changes, objective restrictive, subjective uncertainty factors. In order to maximize the key parameters of the decision project, fashion color hue and tone number, the tone distribution of red color corresponding to the min and max value of L*, a* and b*, are forecasted and estimated in 2011 and re-forecasted in 2012 by the use of the BP neural network. Forecast results in 2011 are basically coincident with actual fashion trends, which shows a scientific, comprehensive, continuous fashion color forecast is done and color forecast theories are enriched.
本课题对国内外有关流行色属性规律分析基础理论、色彩技术、预测及应用方面的研究文献进行了查阅和综述,概述流行色及流行色预测有关概念,从主客观的角度探讨了流行色变化规律,分析流行色预测的影响因素及预测思想方法的优缺点,为流行色的量化预测做准备。基于BP神经网络通过人工智能的方法能够逼近任意关系函数,并且可以根据需要进行自主程序设计,输出更接近最终流行色预测结果的数据,课题对这一预测方法的科学性、全面性、连续性进行了探索。首先搜集权威机构2001—2011年发布的春夏女装流行色色卡,采用科学的CIELab色度测量方法对其进行量化,并将色彩的属性特征数据进行科学归类,建立BP神经网络的样本学习数据库和训练模型,进行流行色色相、色调、单个色相的色调预测与分析验证。
课题将流行色特征通过色卡和量化数据图表两种形式形象地、清晰地表达出来,色卡样本分项统计和图表分析结果直观地显示,春夏女装流行色属性存在不变、渐变与突变的三重特征,这为量化流行色预测提供了依据。研究揭示流行色预测既有色彩量化、流行色规律等有利条件的支撑,也受到流行色突变性变化、客观限制性、主观不确定性等不利因素的影响。为了最大程度准确预测流行色定案中的关键参数,课题进行2011年春夏流行色色相、色调频数以及红色调L*、a*、b*对应的min、max值估计下色调分布的BP神经网络预测与检验以及2012年再预测,2011年各项预测结果与实际流行趋势发布基本吻合,表明该方法能科学、全面、连续地预测流行色,因此本课题研究丰富和发展了流行色预测理论。
With the prevailing of color economy, more and more attention has been paid on the added value brought by fashion color in textile and garment industry, color forecasting has become an indispensable link. Most of the domestic color theories are from abroad because of the late start of China's apparel industry, foreign authorities releases play an important role as reference and domestic enterprises are dependent on the color information released by such institutions as International Fashion Color Association, Cotton Incorporated, Pantone Company and so on. Today, based on the combination of domestic market conditions, more and more domestic institutions can do a comprehensive prediction job in reference to foreign release. However, in the era of the highly developed computer technology, the existing prediction methods at home and abroad still reveal flaws, such as practicality, reachability and the problems of handling the nonlinear variation of fashion color.
This subject searches and summarizes the domestic and international relevant literatures on basic theories of fashion color characteristic rules, color technology, forecast and application in order to have an overview of fashion color and its forecasts, and then explore the changing rules from the perspective of subjective and objective, analyzes the advantages and disadvantages of the forecasting thoughts and impacting forecasting factors for the preparation of quantitative forecast. As the BP neural network can approach any relationship function through artificial intelligence methods, complete self-programming design according to the need and then output data that are closer to the final result, this subject discusses the scientific, comprehensive, continuous characters of this method. Firstly, Women's SS fashion color cards from 2001 to 2011 released by authoritative organ are collected and quantified with a scientific method of CIELab color measurement, then, the color characteristic data are classified as the object of experimental study, at last, fashion color hue , tone and the tone of single color are forecasted and estimated by the establishment of sample learning database and training model of the BP neural network.
This subject clearly illustrate the vivid fashion color characteristics via color cards and quantitative data charts, which shows that there is triple features of women's SS fashion color, constant, gradation and mutation, this provides foundation for quantitative fashion color forecast. Also fashion color forecast not only has a support of favorable conditions like color quantization, fashion color rules, but also received negative influences like abrupt changes, objective restrictive, subjective uncertainty factors. In order to maximize the key parameters of the decision project, fashion color hue and tone number, the tone distribution of red color corresponding to the min and max value of L*, a* and b*, are forecasted and estimated in 2011 and re-forecasted in 2012 by the use of the BP neural network. Forecast results in 2011 are basically coincident with actual fashion trends, which shows a scientific, comprehensive, continuous fashion color forecast is done and color forecast theories are enriched.
引文
[1]余平.计算机在流行色分析预测中的应用[D].天津:天津工业大学, 2004.
[2]常丽霞,高卫东,卢雨正,等.流行色春夏定案特征分析与探讨[J].纺织学报, 2010, 31(3): 99-103.
[3]张毅.流行色预测的量化分析[J].装饰, 2007(9): 51.
[4] J. J. Lina, P. T. Suna, J. J. -R. Chena, et al. Applying gray model to predicting trend of textile fashion colors[J]. Journal of the Textile Institute, 2010, 101(4): 360–368.
[5] Lixia Chang, JiaXie, Weidong Gao, et al. Fashion colour predicting research based on grey theory and rough set theory[C]//IEEE. International Conference on Computer-Aided Industrial Design and Conceptual Design thesis Compilation. Washington: [s.n.]. 2009: 1586-1587.
[6]吴志明,李熠.服装流行色的色彩意向灰色预测[J].纺织学报, 2009, 30(4): 94-99.
[7]周琴.用回归分析法预测服装流行色[D].无锡:江南大学, 2005.
[8] http://baike.baidu.com/view/1548363.htm
[9]胡松.从CNCS色彩标准的建立看纺织品色彩的系统性管理[J].纺织导报, 2010(6): 137.
[10]廖风华.基于PCCS色彩体系的装饰设计色彩规律再认识[J].室内设计, 2009(4): 64-65.
[11]梁昭华.色彩构成[M].北京:中国纺织出版社, 2010: 24.
[12]西蔓色彩研究中心.关注色彩[M].北京:中国轻工业出版社, 2004: 42.
[13]刘宁.色彩的情感与象征意义[J].才智, 2009, 23: 151.
[14] (德)爱娃·海勒.色彩的文化[M].北京:中央编译出版社, 2004: 69.
[15]刘卉,许端清,陈纯.对计算机管理流行色的研究[J].纺织学报, 2001, 22(1): 52.
[16]张新沂,林剑.基于DSP的印刷品在线监控系统[J].包装工程, 2009(6): 52.
[17]滕秀金,邱迦易,曾晓栋.颜色测量技术[M].北京:中国计量出版社, 2008: 40-42. [18 GB/T21898-2008,纺织品颜色表示方法[S].
[19]周顺芬.数字化色标的析色与提色初探[J].艺术探索, 2007, 21(2): 111.
[20] Masanori Kawamura. Roles of color fashion as a reflection of times and color design[J].自動車技術, 2003, 57(5): ?.
[21]金雅庆.流行色在建筑装饰设计中的作用[J].吉林工程技术师范学院学报, 2008, 24(7): 15.
[22]冯素杰,巴妍,邓鹏举.流行色对服装市场营销的影响[J].流行色, 2006(4): 82.
[23] Diane Wanger, Bob Wanger. Fashion, Color Dominate Home Textile Trends[J]. Textile World, 1996, 146(2): 27.
[24]黄元庆.服装色彩学[M].北京:中国纺织出版社, 2004: 106.
[25] (英)汤姆·卡斯迪,特蕾西·黛安.色彩预测与服装流行[M].北京:中国纺织出版社, 2007: 39.
[26] Xiaolei-Wang. Terminal of fashion color[C]. AIC-Color Science for Industry. Zhejiang: [s.n.]. 2007: 239-241.
[27]余平,滑钧凯.计算机在流行色视觉心理分析中的应用[J].天津纺织技, 2004(1): 7.
[28]崔唯,张玮.1996-2005年InterColor春夏色案分析[J].国际纺织品流行趋势, 2007 (3): 27.
[29]李宏伟,王倩梅,洪瑞璘.流行预测[M].北京:中国纺织出版社, 2000: 3.
[30]李熠.基于灰色系统理论的服装流行色预测研究[D].无锡:江南大学, 2007.
[31]白芸.色彩设计[M].西安:陕西人民美术出版社, 1996: 57-58.
[32] Barbara Parker. Color Forecasting -What Will We See Tomorrow[C]. Regional Technical Conference of the Society of Plastics Engineers(SPE) Color and Appearance Division. San Antonio(US): [s.n.]. 2007: 27.
[33]齐小华,高福安.预测理论与方法[M].北京:北京广播学院出版社, 1994: 80.
[34]李熠.评述服装流行色[J].四川丝绸, 2007(1): 37.
[35]李熠,吴志明.服装流行色预测方法及量化思想比较分析[J].纺织科技进展, 2006(2): 82.
[36] Rita Perna,李宏伟,王倩梅.流行预测[M].北京:中国纺织出版社, 2000: 17.
[37]陈林龙.采用FPV法预测流行色[J].流行色, 1991 (4): 8-9.
[38]陈林龙.采用FPV法预测流行色(续一) [J].流行色, 1992 (1): 16.
[39]陈林龙.采用FPV法预测流行色(续完) [J].流行色, 1992 (2): 6-7.
[40]虞台文,吴瑞卿,魏朝宏.灰色系统理论於流行色预测上的研究[J].设计学报, 1998 (12): 61-71.
[41]张全,康文科,明新国.基于粗集的产品色彩预测系统研究[J].计算机工程与应用, 2008, 44(11): 31.
[42]李鹏.流行色信息系统的研究[D].西安:西北纺织工学院, 2000.
[43]肖彬,崔唯.新论流行色的基本特征[J].流行色, 2008 (2): 12-17.
[44]傅炯,韩挺.产品流行色基本原理和研究方法[A].中国流行色协会.当代亚洲色彩应用—第四届亚洲色彩论坛论文集[C].北京:中国纺织出版社, 2007: 41.
[45]余平,滑钧凯.计算机在流行色管理方面的应用[J].毛纺科技, 2003 (5): 15.
[46]戴艳.1996-2005年国际流行色女装定案与国内女装色彩分析与研究[D].北京:北京服装学院, 2006.
[47]肖楠.女装品牌风格定位下的色彩传承性分析[J].美术之友, 2009(6): 159.
[48]王林玉,王厉冰.人工神经网络在预测服装及面料舒适性方面的探讨[J].青岛大学学报(工程技术版), 2006, 21(1): 83.
[49]韩力群.人工神经网络实用教程[M].北京:北京邮电大学出版社, 2006: 5.
[50]史忠植.智能科学[M].北京:清华大学出版社, 2006: 59.
[51]王影. BP神经网络在股市预测中的应用与研究[D].四川:电子科技大学, 2006.
[52]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社, 2001: 41-42.
[53]鲁娟娟,陈红.BP神经网络的研究进展[J].控制工程, 2006, 13(5): 449-456.
[2]常丽霞,高卫东,卢雨正,等.流行色春夏定案特征分析与探讨[J].纺织学报, 2010, 31(3): 99-103.
[3]张毅.流行色预测的量化分析[J].装饰, 2007(9): 51.
[4] J. J. Lina, P. T. Suna, J. J. -R. Chena, et al. Applying gray model to predicting trend of textile fashion colors[J]. Journal of the Textile Institute, 2010, 101(4): 360–368.
[5] Lixia Chang, JiaXie, Weidong Gao, et al. Fashion colour predicting research based on grey theory and rough set theory[C]//IEEE. International Conference on Computer-Aided Industrial Design and Conceptual Design thesis Compilation. Washington: [s.n.]. 2009: 1586-1587.
[6]吴志明,李熠.服装流行色的色彩意向灰色预测[J].纺织学报, 2009, 30(4): 94-99.
[7]周琴.用回归分析法预测服装流行色[D].无锡:江南大学, 2005.
[8] http://baike.baidu.com/view/1548363.htm
[9]胡松.从CNCS色彩标准的建立看纺织品色彩的系统性管理[J].纺织导报, 2010(6): 137.
[10]廖风华.基于PCCS色彩体系的装饰设计色彩规律再认识[J].室内设计, 2009(4): 64-65.
[11]梁昭华.色彩构成[M].北京:中国纺织出版社, 2010: 24.
[12]西蔓色彩研究中心.关注色彩[M].北京:中国轻工业出版社, 2004: 42.
[13]刘宁.色彩的情感与象征意义[J].才智, 2009, 23: 151.
[14] (德)爱娃·海勒.色彩的文化[M].北京:中央编译出版社, 2004: 69.
[15]刘卉,许端清,陈纯.对计算机管理流行色的研究[J].纺织学报, 2001, 22(1): 52.
[16]张新沂,林剑.基于DSP的印刷品在线监控系统[J].包装工程, 2009(6): 52.
[17]滕秀金,邱迦易,曾晓栋.颜色测量技术[M].北京:中国计量出版社, 2008: 40-42. [18 GB/T21898-2008,纺织品颜色表示方法[S].
[19]周顺芬.数字化色标的析色与提色初探[J].艺术探索, 2007, 21(2): 111.
[20] Masanori Kawamura. Roles of color fashion as a reflection of times and color design[J].自動車技術, 2003, 57(5): ?.
[21]金雅庆.流行色在建筑装饰设计中的作用[J].吉林工程技术师范学院学报, 2008, 24(7): 15.
[22]冯素杰,巴妍,邓鹏举.流行色对服装市场营销的影响[J].流行色, 2006(4): 82.
[23] Diane Wanger, Bob Wanger. Fashion, Color Dominate Home Textile Trends[J]. Textile World, 1996, 146(2): 27.
[24]黄元庆.服装色彩学[M].北京:中国纺织出版社, 2004: 106.
[25] (英)汤姆·卡斯迪,特蕾西·黛安.色彩预测与服装流行[M].北京:中国纺织出版社, 2007: 39.
[26] Xiaolei-Wang. Terminal of fashion color[C]. AIC-Color Science for Industry. Zhejiang: [s.n.]. 2007: 239-241.
[27]余平,滑钧凯.计算机在流行色视觉心理分析中的应用[J].天津纺织技, 2004(1): 7.
[28]崔唯,张玮.1996-2005年InterColor春夏色案分析[J].国际纺织品流行趋势, 2007 (3): 27.
[29]李宏伟,王倩梅,洪瑞璘.流行预测[M].北京:中国纺织出版社, 2000: 3.
[30]李熠.基于灰色系统理论的服装流行色预测研究[D].无锡:江南大学, 2007.
[31]白芸.色彩设计[M].西安:陕西人民美术出版社, 1996: 57-58.
[32] Barbara Parker. Color Forecasting -What Will We See Tomorrow[C]. Regional Technical Conference of the Society of Plastics Engineers(SPE) Color and Appearance Division. San Antonio(US): [s.n.]. 2007: 27.
[33]齐小华,高福安.预测理论与方法[M].北京:北京广播学院出版社, 1994: 80.
[34]李熠.评述服装流行色[J].四川丝绸, 2007(1): 37.
[35]李熠,吴志明.服装流行色预测方法及量化思想比较分析[J].纺织科技进展, 2006(2): 82.
[36] Rita Perna,李宏伟,王倩梅.流行预测[M].北京:中国纺织出版社, 2000: 17.
[37]陈林龙.采用FPV法预测流行色[J].流行色, 1991 (4): 8-9.
[38]陈林龙.采用FPV法预测流行色(续一) [J].流行色, 1992 (1): 16.
[39]陈林龙.采用FPV法预测流行色(续完) [J].流行色, 1992 (2): 6-7.
[40]虞台文,吴瑞卿,魏朝宏.灰色系统理论於流行色预测上的研究[J].设计学报, 1998 (12): 61-71.
[41]张全,康文科,明新国.基于粗集的产品色彩预测系统研究[J].计算机工程与应用, 2008, 44(11): 31.
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