基于BP神经网络和遗传算法的结构优化设计

现代航空发动机不断追求提高推重比,优化其零部件的结构设计日益重要。传统结构优化方法耗时多且不易掌握。针对这一问题,本文提出了将BP神经网络和遗传算法相结合用于结构优化设计的方法,并编制了相应的计算程序,实现了一个含9个设计变量的发动机盘模型的结构优化计算。计算证明,与传统结构优化方法相比,此方法计算速度快、精度良好。

Structure Design Optimization Based on BP-Neural Networks and Genetic Algorithms

Owing to the increasing demand for raising the thrust weight ratio of modern aero engine,it is very important to optimize the structures of the components.Traditional optimization methods of structure design are time consuming and hard to be put into practice.So,in this paper,a new method of structure design optimization is induced to which both BP neural networks and genetic algorithms (in short:BPN GA) are applied.A program which contains 9 variables is designed for the structure optimization of a disk model with the BPN GA method,which proves that it has better calculating rate and precision than those with traditional optimization methods.