Dynamic learning rate neural network training and composite structural damage detection

A new learning procedure, called dynamic learning rate steepest descent (DSD) method, is proposed for training neural networks. Based on the simple steepest descendent method, the proposed method improves the learning convergence speed significantly without increasing the computational effort, the memory cost, the algorithm simplicity, and the computational locality in the standard layered error backpropagating training algorithm. Through numerical experiments, the current method is shown to have much better learning ability than that of the standard constant learning rate steepest descent method and the accelerated steepest descendent method. The numerical experiments also indicate that the current method is robust to the selection of the initial learning rate, which is critical in the standard steepest descent method. It is also shown to be efficient. The CPU time increase, due to extra operations in the DSD algorithm, is negligible. The DSD method is then used to train a neural network for direct identification of composite structural damage through structural dynamic responses. The result indicates that neural network can be used for real-time flaw detections and advanced structural health monitoring.