数据驱动的时延神经网络动载荷识别方法

A data-driven dynamic load identification method based on time-delay neural networks

载荷识别是指根据测量的结构响应重构结构载荷的问题,属于力学中的反问题。本文提出了一种基于时延神经网络的载荷识别方法,通过实验和仿真相结合的数值算例验证表明,这一方法相比于一般的反向传播神经网络具有更高的识别精度;在时延神经网络的基础上,引入了统计池化的思想,并与普通的神经网络载荷识别方法相比较,证明了该方法在不同强度的噪声环境下均具有良好的识别效果;基于上述载荷识别方法,提出了一种基于粒子群优化算法的传感器布局优化策略,相比于随机的传感器布局,优化后的传感器布局可以在考虑传感器安装间距的同时,将载荷识别误差降低90%以上,有效提高了载荷识别精度。

The problem of load identification denotes identifying loads based on the measurement of structural responses,which is the inverse problem in structural dynamics.A load identification method based on time-delay neural network is proposed in this paper,and numerical examples based on simulation and experiments are provided to show that the method overperforms normal backpropagation neural network in accuracy of identification.Additionally,statistic pooling is introduced on the basis of the method,and it is proved that the method performs well in noisy environment compared with BP neural networks.based on the load identification methods mentioned above,a sensor placement optimization based on particle swarm optimization algorithm is proposed,and the optimal sensor placement is able to reduce the error of identification by 90%compared with the random sensor placements,meanwhile the minimum spacing of installation among sensors is also ensured during the optimization.