基于改进BP网络的地震动信号目标识别

应用人工神经网络进行目标识别是当前模式识别的重要方法之一。前向多层神经网络及其ＢＰ算法发展较为成熟的一种。该对ＢＰ算法加以改进，使得其性能所提高，收敛速度加快。

Microseismic Signal Targets Identification Based on Improved BP Neural Networks

It is one of the important methods of pattern recognition to apply neural networks to target classification. Forward propagation multi layers neural networks and its BP algorithm are used widely. In this article, some measures are taken to improve BP algorithm, and to make its performance better and its convergence speed quicker.The seismic sensor is an essential sensor in battlefield watching system.By testing,a great number of seismic signals are obtained on footsteps, wheeled vehicle and tank. These signals are processed using wavelets transform and wavelets package. The energy spectrum features of these signals are extracted, and two series connected BP neural networks identify them. The results of 94.5% proper identification are attained.