Multi-objective Optimization of the Hydraulic Press Crossbeam Based on Neural Network and Pareto GA

摘要：

The structures approximation analysis technology is studied based on neural network.The back-propagation neural network model corresponding to the size parameters of the hydraulic press crossbeam and its displacement or stress is generated to replace the original finite element model in this paper.Using the saturated multi-level table of orthogonal arrays to choose the trained samples could make the neural network has extensive representations.In order to search the minimization of the crossbeams volume and displacement,the Pareto GA is used and the detailed technique is described.The optimization result is satisfactory,which shows the combination of the neural network and Pareto GA provides a new scientism method on solving the complex solid structures multi-objective optimization.

关键词： Neural network multi-objective optimization Pareto GA orthogonal design structures approximation

作者: Liu Qian Bian Xue-liang

作者单位: School of Mechanical Engineering Hebei University of Technology Tianjin City,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 52-55

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）