Integrating radial basis function networks with case-based reasoning for product design
Integrating radial basis function networks with case-based reasoning for product design
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DOI：
10.1016/j.eswa.2008.06.099
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发表时间：
2009-04
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影响因子：
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通讯作者：
Sabum Jung;Taesoo Lim;Dongsoo Kim
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文献类型：
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作者：
Sabum Jung;Taesoo Lim;Dongsoo Kim
本文提出了一个基于实例的设计专家系统，它可以自动确定产品的设计值。本文主要研究电子工业中显示器的核心部件荫罩板的设计问题。在基于实例的推理（CBR）中，检索相似实例并使其准确地满足设计规范是非常重要的。值得注意的是，在自动化的适应过程中的困难，阻止了设计师能够轻松有效地使用设计专家系统。在本文中，我们提出了一种混合的方法结合CBR和人工神经网络，以解决在适应过程中出现的问题。首先，我们构建了一个径向基函数网络（RBFN）的代表性的情况下，通过K均值聚类。然后，使用网络调整与当前问题最相似的代表性案例。所提出的方法背后的基本原理进行了讨论，并从真实的荫罩设计获得的实验结果。使用设计专家系统，设计人员可以减少设计时间和错误，提高设计的整体质量。此外，专家系统有利于设计师之间的设计知识的有效共享。
This paper presents a case-based design expert system that automatically determines the design values of a product. We focus on the design problem of a shadow mask which is a core component of monitors in the electronics industry. In case-based reasoning (CBR), it is important to retrieve similar cases and adapt them to meet design specifications exactly. Notably, difficulties in automating the adaptation process have prevented designers from being able to use design expert systems easily and efficiently. In this paper, we present a hybrid approach combining CBR and artificial neural networks in order to solve the problems occurring during the adaptation process. We first constructed a radial basis function network (RBFN) composed of representative cases created by K-means clustering. Then, the representative case most similar to the current problem was adjusted using the network. The rationale behind the proposed approach is discussed, and experimental results acquired from real shadow mask design are presented. Using the design expert system, designers can reduce design time and errors and enhance the total quality of design. Furthermore, the expert system facilitates effective sharing of design knowledge among designers.