期刊論文
| 學年 | 90 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2002-07-01 |
| 作品名稱 | Generalized Hopfield network based structural optimization using sequential unconstrained minimization technique with additional penalty strategy |
| 作品名稱（其他語言） | |
| 著者 | Shih, C. J.; Yang, Y. C. |
| 單位 | 淡江大學機械與機電工程學系 |
| 出版者 | Elsevier |
| 著錄名稱、卷期、頁數 | Advances in Engineering Software 33(7-10), pp.721-729 |
| 摘要 | This paper presents and examines a neuron-like framework of the generalized Hopfield network (GHN) that is capable to solve nonlinear engineering optimization problems with mixed discrete, integer and real continuous variables. The sequential unconstrained minimization technique (SUMT) was applied to construct the GHN for dealing with the design constraints. An additional penalty function for dealing with the discrete and integer variables was then imposed on the formulation of SUMT to construct an energy function of GHN for formulating the neuron-like dynamical system. The numerical solution process for such a dynamic system is simply solving a set of simultaneous first-order ordinary differential equations (ODE) that is the main feature of this optimization method. The experimental examples showed the presenting strategy is reliable. The suitable values or the adaptation technique for some parameters in computation was discussed in the paper. The presenting strategy indeed provides an alternative way of handling the engineering optimization dynamically and expands the usage of ODE. An asymmetrical three-bar truss design, a reinforced concrete beam design and a 10-bar structural design are contributed to illustrate the presenting neuron-like network method. |
| 關鍵字 | Generalized Hopfield network;Nonlinear programming;Sequential unconstrained minimization technique;Penalty function;Ordinary differential equation;Structural optimization |
| 語言 | en |
| ISSN | 0965-9978 |
| 期刊性質 | 國內 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/45839 ) |