基于BP神经网络的罐式车辆配色与评价方法研究
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摘要
为有效降低产品配色对专业知识的依赖,提高产品配色设计的效率,提出一种基于BP神经网络的复杂产品配色方法。该方法结合色彩心理语义词汇分析及色彩库的建立,运用HSV色彩编码方法,借助MATLAB软件,在保证网络收敛速度和误差收敛合理的情况下,通过对隐藏层结点设定不同的结点数,经过训练迭代,获得配色方案,并运用模糊综合评价法对配色方案进行评价,细化优选方案,完成最终配色设计。以某型罐式车辆为例,进行配色设计与评价,验证了该方法的可行性和有效性。
In order to reduce the dependence of product color matching on professional knowledge effectively and improve the efficiency of product color matching design, a complex color matching method based on BP neural network was proposed.This method combined with color psychology semantic vocabulary analysis and color library establishment using HSV color coding method. The color matching scheme was obtained through the different node numbers settings on hidden layer nodes under the condition of ensuring network convergence rae and error convergence. Fuzzy comprehensive evaluation method was perform to evaluate the color matching scheme, refine preferred scheme, and complete the final color matching design. A tank vehicle was taken as an example to perform color matching design and evaluation. The feasibility and effectiveness of the proposed color matching method was verified.
In order to reduce the dependence of product color matching on professional knowledge effectively and improve the efficiency of product color matching design, a complex color matching method based on BP neural network was proposed.This method combined with color psychology semantic vocabulary analysis and color library establishment using HSV color coding method. The color matching scheme was obtained through the different node numbers settings on hidden layer nodes under the condition of ensuring network convergence rae and error convergence. Fuzzy comprehensive evaluation method was perform to evaluate the color matching scheme, refine preferred scheme, and complete the final color matching design. A tank vehicle was taken as an example to perform color matching design and evaluation. The feasibility and effectiveness of the proposed color matching method was verified.
引文
[1]张琳,赵静.产品色彩设计意象评价的数理方法研究[J].机械设计,2014,31(12):108-111.
[2]林燕萍.浅析服饰流行色预测的方法[J].印染助剂,2017,34(6):7-9.
[3]方正,王仁彬,陈思媛.基于GA-BP神经网络的塑料X射线吸收光谱的辨识[J].应用化工,2018,11(5):1-6.
[4]袁培飒,陈锐,林涛,等.基于遗传算法和神经网络的软件界面美感建模[J].四川大学学报:自然科学版:,2015,52(2):269-274.
[5]赵黎,杨连贺,黄新.基于多目标蜂群优化算法的计算机辅助配色[J].计算机集成制造系统,2018,24(2):381-389.
[6] McClelland J,Rumelhart D. Parallel distributed processing:explorations in the microstructure of cognition[M]. Cambridge:MIT Press,1986.
[7] Hornik K,Stinchcombe M,White H. Mutilayer feed forward networks are universal approximators[J]. Neural Networks,1989(2):359-366.
[8]师晶晶. HSV色彩空间下三边滤波的Retinex图像增强算法研究[J].自动化与仪器仪表,2018(12):12-14.
[9] Hsiao S W,Chiu F Y,Lu S H. Product-form design model based on genetic algorithms[J]. International Journal of Industrial Ergonomics,2010,40(3):237-246.
[10] Kurt Hornik. Approximation capabilities of multilayer feed forward networks neural[J]. Neural Network,1991(4):551-557.
[11] JB/T 4711—2003压力容器涂敷与运输包装[S].
[12] GB 13392—2005道路运输危险货物车辆标志[S].
[13] GB 20300—2018道路运输爆炸品和剧毒化学品车辆安全技术条件[S].
[14]张燕,余隋怀,陈登凯,等.基于遗传蚁群算法的产品配色方法研究[J].图学学报,2013,34(3):79-84.
[15]董长虹. Matlab神经网络与应用[M].北京:化学工业出版社,2005.
[16]王飞,郑张丽,郭静静,等.基于神经网络的公路工程造价预测模型[J].河北工程大学学报:自然科学版,2014,31(4):102-104.
[17]胡志刚,余隋怀,闫红玲,等.基于案例知识的数控机床感性配色研究[J].机械设计,2017,34(3):117-120.
[2]林燕萍.浅析服饰流行色预测的方法[J].印染助剂,2017,34(6):7-9.
[3]方正,王仁彬,陈思媛.基于GA-BP神经网络的塑料X射线吸收光谱的辨识[J].应用化工,2018,11(5):1-6.
[4]袁培飒,陈锐,林涛,等.基于遗传算法和神经网络的软件界面美感建模[J].四川大学学报:自然科学版:,2015,52(2):269-274.
[5]赵黎,杨连贺,黄新.基于多目标蜂群优化算法的计算机辅助配色[J].计算机集成制造系统,2018,24(2):381-389.
[6] McClelland J,Rumelhart D. Parallel distributed processing:explorations in the microstructure of cognition[M]. Cambridge:MIT Press,1986.
[7] Hornik K,Stinchcombe M,White H. Mutilayer feed forward networks are universal approximators[J]. Neural Networks,1989(2):359-366.
[8]师晶晶. HSV色彩空间下三边滤波的Retinex图像增强算法研究[J].自动化与仪器仪表,2018(12):12-14.
[9] Hsiao S W,Chiu F Y,Lu S H. Product-form design model based on genetic algorithms[J]. International Journal of Industrial Ergonomics,2010,40(3):237-246.
[10] Kurt Hornik. Approximation capabilities of multilayer feed forward networks neural[J]. Neural Network,1991(4):551-557.
[11] JB/T 4711—2003压力容器涂敷与运输包装[S].
[12] GB 13392—2005道路运输危险货物车辆标志[S].
[13] GB 20300—2018道路运输爆炸品和剧毒化学品车辆安全技术条件[S].
[14]张燕,余隋怀,陈登凯,等.基于遗传蚁群算法的产品配色方法研究[J].图学学报,2013,34(3):79-84.
[15]董长虹. Matlab神经网络与应用[M].北京:化学工业出版社,2005.
[16]王飞,郑张丽,郭静静,等.基于神经网络的公路工程造价预测模型[J].河北工程大学学报:自然科学版,2014,31(4):102-104.
[17]胡志刚,余隋怀,闫红玲,等.基于案例知识的数控机床感性配色研究[J].机械设计,2017,34(3):117-120.