一个以情感为导向的产品评估模型,基于BP神经网络和改进的普斯特-谢弗理论
Kun Wang1, Kexiang Li2, Xueqian Jiang3
1Institute of Fine Arts and Design, Quanzhou Normal University, Quanzhou, 362000, China. willkende@qztc.edu.cn.
Scientific reports
|November 10, 2025
概括
本研究介绍了一种使用反向传播 (BP) 神经网络和改进的Dempster-Shafer (D-S) 证据理论用于以用户为中心的产品设计的混合框架. 它量化了情感感知,克服了主观性,并改进了多源数据融合,以更好地评估产品.
科学领域:
- 人与计算机的交互
- 产品设计工程 产品设计工程
- 人工智能的人工智能
背景情况:
- 在产品开发中评估用户情感感知是关键的,但受到主观偏见和低效的多源数据融合的挑战.
- 现有的方法往往难以准确地整合各种用户反和专家意见.
- 对于以用户为中心的设计实践,需要进行定量情感评估.
研究的目的:
- 提出一个混合产品评估框架,整合反向传播 (BP) 神经网络和改进的Dempster-Shafer (D-S) 证据理论.
- 解决主观偏见问题,提高情感感知多源信息融合的效率.
- 为以用户为中心的产品设计提供定量决策支持.
主要方法:
- 构建的输入向量结合了产品设计和用户特征.
- 利用多层BP网络来学习产品属性和用户情绪之间的非线性关系,生成初步信念分配 (BPA).
- 优化DS证据理论与基于证据距离的权重策略,以融合相互矛盾的专家意见并产生统一的情感评估.
主要成果:
- 混合框架有效量化产品评估中的情感维度,使用沙发作为案例研究来证明这一点.
- 确定了影响用户情绪的关键设计元素.
- 与现有方法相比,在多源数据处理中表现出卓越的稳定性,适应性和融合精度.
结论:
- 拟议的混合BP-D-S模型为产品设计中的定量情感评估提供了一个强大的解决方案.
- 它有效地克服了主观性的局限性,并提高了多源数据的融合效率.
- 为以用户为中心的产品开发提供了有价值的定量决策支持,并提高了可扩展性.
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