A design space exploration method using Artificial Neural Networks and metamodeling

摘要：

This paper suggests a design space exploration method using Artificial Neural Networks and metamodeling to systematically reduce the design space to a relatively small region. This method consists of three major steps. Firstly, self-organizing maps is employed to analyze design variables and objective function(s) with the original samples as preliminary reduction optimization of the initial large design space. Successively, resampling within the preliminary reduction space, clustering sample points using the fuzzy c-means clustering method with the given number of cluster, and choosing the most attractive cluster to construct kriging model and identify the design optimum within the reduced design space in the last step. The accuracy and validity of proposed methodology is proved by a heat exchanger design problem. It is found that the proposed method can intuitively capture promising design regions in which it is efficient to acquire the global or near-global desigm optimum.

关键词： Design optimization Self-organizing maps Fuzzy clustering Metamodeling

作者: Li Chi Haobo Qiu ZhenZhong Chen Li Ke

作者单位: The State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Sc The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of S

会议类型: 国际会议

会议名称: 第三届产品开发与可靠性进展国际会议

会议地点: 武汉

会议语种:英文

页码: 200-205

在线出版日期: 2012-07-29（万方平台首次上网日期，不代表论文的发表时间）