Sequential Approximate Optimization Using Radial Basis Function network

摘要：

Sequential approximate optimization (SAO) is one of the attractive methods for solving engineering problems.One of the successful keys in SAO is the sampling strategy.In particular, a function called the density function is constructed for global approximation with the RBF network.The basic of density function idea is very simple.The SAO algorithm with the RBF network is proposed.Through numerical examples, the validity of the proposed SAO algorithm is examined.

关键词： Sequential Approximate Optimization Radial Basis Function network Density Function

作者: Satoshi Kitayama Masao Arakawa Koetsu Yamazaki

作者单位: College of Science and Engineering,Kanazawa University,Kanazawa,Japan Faculty of Engineering,Kagawa University,Takamasu,Japan

会议类型: 国际会议

会议名称: The 7th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems 第七届中日韩结构和机械系统优化会议

会议地点: 黄山

会议语种:英文

页码: 1-8

在线出版日期: 2012-06-18（万方平台首次上网日期，不代表论文的发表时间）