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Research on delamination monitoring for composite structures based on HHGA-WNN

机译：基于HHGA-WNN的复合结构脱层监测研究

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摘要

Due to the deficiencies of the training algorithms for available wavelet 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 wavelet neural network. The hybrid algorithm was able to determine the structure and parameters of the wavelet 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 wavelet neural network to predict the delamination location and its extent. The simulation demonstrates that the wavelet neural network based on hybrid hierarchy genetic algorithm is robust, promising and converges very fast.
机译：针对现有的用于结构健康监测的小波神经网络训练算法的不足，提出了一种新的混合层次遗传算法，将层次遗传算法和最小二乘相结合，改善了小波神经网络的学习过程。混合算法能够同时确定小波神经网络的结构和参数。该算法利用自适应交叉和变异概率加快遗传速度，避免早熟的发生。使用有限元方法计算了具有不同假定分层大小和位置的玻璃/环氧树脂层压板梁的模态频率，并将其输入到小波神经网络中，以预测分层位置及其程度。仿真表明，基于混合层次遗传算法的小波神经网络具有鲁棒性，良好的前景和收敛速度。

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