利用模态编码进行结构损伤位置识别的自联想储存器神经网络方法

 提出了一种基于自联想储存器神经网络的结构损伤识别方法，该网络的训练数据为经编码后的结构模态向量。和传统BP网络相比，这种方法收敛性能较好且不易陷入局部极小值。另外，为判断识别结果的正确性，提出了一种基于向量间距离的可靠性分析方法。最后，以一个悬臂梁为算例验证了该方法的有效性和可行性。

Auto-associated Memory Neural Network Method of Structure Damage Position Detection Using Mode Coding

This paper discusses the algorithms of the auto-associated memory neural network and presents a novel approach for structural damage detection which is based on the auto-associated memory neural network. The training patterns are different modal vectors of the structure when structural damage happens in different locations. In order to make use of the auto-associated memory neural network to identify structural damage location effectively, a totally new coding method is presented which coverts the modal vectors of structures into code before training the neural network. This approach has eminent convergence properties and does not have to get stuck in local minima as compared with the BP neural network. In addition, a reliability analysis method on the basis of the theory of vector distance is developed to confirm the effectiveness of detection results. The example of a cantilever beam is given to demonstrate and verify the presented approach and it is found that the damage identification method based on the auto-associated memory neural network is effective.