| 摘要: |
| 目的 为实现特定感性意象下的产品CMF精准选定与量化，结合BP神经网络和线性回归提出一种产品CMF决策模型。方法 通过文本挖掘形式确定用户感性意象，根据HSV色彩模型与选定的康复辅具的材质与工艺构建CMF要素空间，并基于设计要素空间形成海量CMF方案，同时根据选定感性意象对方案加以评价，获得感性意象与CMF单一设计要素的定性映射关系。将CMF方案编码后与感性意象评价值结合，并通过BP神经网络以定量方式构建CMF决策模型，筛选出最优色彩区间、材质及工艺。对选中色彩区间再次细分出设计方案并进行评价，通过线性回归得到色彩回归方程，从而构建产品CMF的综合决策模型。结果 以膝关节支具为例进行实例研究，通过BP神经网络构建的一阶CMF决策模型预测值与期望值的均方误差MSE为0.038 13，且预测结果与定性映射关系基本一致，表明该阶模型可信度较高且精度良好。利用线性回归构建的二阶决策模型P值小于0.01，表明H、S、V的数值与感性意象评价值具有显著相关性，证明了该CMF决策模型的可行性。结论 构建的CMF决策模型在产品设计领域具有一定的通用性，能够有效实现康复产品CMF的精准选择与量化，在定性和定量层面指导康复产品CMF决策的优选和创新。 |
| 关键词: 产品设计 康复辅具 感性意象 CMF决策模型 BP神经网络 |
| DOI：10.19554/j.cnki.1001-3563.2023.12.016 |
| 分类号:TB472 |
| 基金项目:国家社科基金艺术学项目(22BG125) |
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| Product CMF Decision Model Based on Perceptual Image and BP Neural Network |
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SUN Li, ZHANG Shuo, QIN Zhong-zhi, WU Jian-tao, LI Jiang-nan, LI Man-po
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(Yanshan University, Hebei Qinhuangdao 066004, China)
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| Abstract: |
| The work aims to develop a product CMF decision model by integrating BP neural network and linear regression in order to accomplish accurate selection and quantification of product CMF under certain perceptual images. Through text mining, the user's perceptual image was identified, a CMF element space was created according to the HSV color model and the material and process of chosen rehabilitation aids, a large number of CMF solutions based on the design element space were formed, and the solutions were assessed in accordance with the chosen perceptual image to obtain a qualitative mapping relationship between the perceptual image and a single design element of CMF. The CMF solutions were coded and integrated with the perceptual image evaluation value and the CMF decision model was established by quantitative method through BP neutral network to identify the best color space, material and method. The chosen color intervals were separated into design solutions and evaluated. The color regression equation was then created by linear regression, and the CMF decision model was quantitatively built by BP neural network. With knee brace as an example, the case study was carried out, the first-order CMF decision model developed by BP neural network had an MSE of 0.038 13 between the predicted and expected values, and the prediction results were essentially consistent with the qualitative mapping relationship, demonstrating the high accuracy and reliability of this order of the model. The values of H, S, and V were highly associated with the perceptual image assessment value, as shown by the second-order decision model's p-value of less than 0.01, which demonstrated the viability of the CMF decision model. The developed CMF decision model has some versatility in the area of product design and is capable of realizing correct CMF selection and quantification for rehabilitation items as well as directing preferential CMF decision and innovation at both the qualitative and quantitative levels. |
| Key words: product design rehabilitation aids perceptual image CMF decision model BP neural network |
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