论文部分内容阅读
为满足消费者对产品造型的感性意象需求,提出了基于支持向量机和粒子群算法的产品意象造型优化设计方法。首先确定目标意象、代表性样本和造型设计参数,进行产品感性意象调查;然后应用支持向量机获得“造型设计参数-产品感性意象”之间的映射关系,建立产品造型意象评价系统;最后以代表性样本为初始种群,以意象评价为适应度评估,利用粒子群算法建立产品意象造型优化设计系统。以汽车轮廓优化设计进行实例研究,结果表明该方法较好地模拟了设计思维,可为产品概念设计提供有效的辅助与支持。
In order to meet consumers’ emotional image requirements of product modeling, an optimization design method of product image based on support vector machines and particle swarm optimization is proposed. Firstly, the target image, representative sample and modeling design parameters are determined to investigate the product’s perceptual image. Then the mapping relationship between the “model design parameters and the product’s perceptual image” is obtained by using support vector machine, and the product modeling evaluation system is established. Finally, Taking the representative sample as the initial population, using the image evaluation as the fitness assessment, the particle swarm optimization algorithm was used to establish the product imagery optimization design system. The case study of automobile contour optimization design shows that this method can well simulate the design thinking and provide effective support and support for product concept design.