Application of adaptive HHGA─RBF Neural Network To Damage Monitoring for Composite Structures

摘要：

Due to the deficiencies of the training algorithms for available radial basis function(RBF)neural network used for structural health monitoring,a new hybrid hierarchy genetic algorithm was introduced by combining hierarchy genetic algorithm and least-square method to improve the learning procedure of RBF neural network.The hybrid algorithm was able to determine the structure and parameters of the RBF neural network simultaneously.In this algorithm,adaptive crossover and mutation probability were used to accelerate the genetic speed and avoid the occurrence of prematurity.The modal frequencies of a glass/epoxy laminates beam with varying assumed delamination sizes and locations were computed using finite element method and fed into the radial basis function neural network to predict the delamination location and its extent.The simulation demonstrates that the radial basis function neural network based on hybrid hierarchy genetic algorithm is robust,promising and converges very fast.

关键词： hybrid hierarchy genetic algorithm RBF Neural Network Structural health Monitoring

作者: Zheng-qiang LI Shi-jie ZHENG

作者单位: Aeronautical Science Key Lab of Smart Materials & Structures,Nanjing University of Aeronautics & Astronautics,Nanjing,210016

会议类型: 国际会议

会议名称: The World Forum on Smart Materials and Smart Structures Technology(SMSST07)(2007年世界智能材料与智能结构技术论坛)

会议地点: 重庆·南京

会议语种:英文

在线出版日期: 2007-05-01（万方平台首次上网日期，不代表论文的发表时间）