利用时延神经网络的动载荷倒序识别

将时延神经网络引入动载荷识别研究中，结合时延神经网络的"记忆"特性、因果有限长冲激响应（FIR）系统理论与振动响应的求解原理，提出一种利用时延神经网络的时域动载荷倒序识别方法。对一个受两点随机动载荷作用的舵面模型结构进行载荷识别验证实验，结果表明，用本文方法识别的两个激励点上识别载荷样本的时间序列与真实载荷样本的时间序列之间的均方根误差分别为0.635 4和2.543 7，识别载荷样本时间序列与真实载荷样本时间序列的相关系数分别为0.965 7和0.826 2，功率谱密度曲线也能够较好吻合。本文提出的方法具有不需要结构动力学模型、识别精度高的优点。

Reversed time sequence dynamic load identification method using time delay neural network

The time delay neural network, extensively applied in speech recognition, is introduced to identify random dynamic loads. Combining the "memory" property of the time delay neural network with the causal Finite-Impulse-Response (FIR) system theory and the steady response solution of the vibration theory, we propose a reversed time sequence dynamic load identification method. Experimental verification of the proposed method is conducted using an aircraft rudder model excited by two-point random loads. The results demonstrate that the root mean square errors between the time histories of the identified and real dynamic load samples on the two loading points are 0.635 4 and 2.543 7, respectively, and the correlation coefficients are 0.9657 and 0.8262, respectively. The curve of the power spectral density function between the identified and real dynamic loads on the two loading points coincides fairly well. The proposed dynamic load identification method has the advantage of high precision and requires no structural modelling.