基于粒子群的改进智能算法在载荷识别中的应用

来源期刊:中南大学学报(自然科学版)2019年第2期

论文作者:曲先强 谢兵 谢博群 张猛

文章页码:343 - 350

关键词:载荷识别;反问题;粒子群优化算法(PSO);遗传算法

Key words:load identification; inverse problem; particle swarm optimization algorithm(PSO); genetic algorithm

摘    要:针对粒子群优化算法(PSO)无法处理反求问题中的病态问题,基于粒子群优化算法,通过遗传算法对粒子群优化算法进行改进,提出一种改进的粒子群优化算法(GAPSO),通过载荷识别对该方法进行验证,并应用于静态载荷识别和动态载荷识别算例中。研究结果表明:改进后的粒子群优化算法既能使粒子群优化算法处理病态问题,又提高了反求问题的求解精度。

Abstract: Considering that particle swarm optimization algorithm(PSO) cannot deal with ill-posed problem, an improved particle swarm optimization algorithm(GAPSO) was proposed by genetic algorithm based on particle swarm optimization. This method was verified by common inversion problems such as load identification. Finally, the improved optimization algorithm was applied in static load identification and dynamic load identification. The results show that the improved particle swarm optimization algorithm can not only solve ill-posed problems, but also improve the accuracy of inverse problem.

有色金属在线官网  |   会议  |   在线投稿  |   购买纸书  |   科技图书馆

中南大学出版社 技术支持 版权声明   电话:0731-88830515 88830516   传真:0731-88710482   Email:administrator@cnnmol.com

互联网出版许可证:(署)网出证(京)字第342号   京ICP备17050991号-6      京公网安备11010802042557号