介绍了自适应遗传算法优化的BP神经网络(AGA-BP)算法在炮射地面震动传感器目标识别中的应用。首先针对BP神经网络可能未收敛到全局最小点的缺陷,提出自适应遗传算法与BP神经网络结合的一种优化算法。之后进行仿真实验并对履带和轮式车辆的采样信号进行时频分析,利用小波变换提取特征值。最后利用优化后的算法与传统算法进行了样本训练和识别,对比结果表明该方法能减少识别误差。

An adaptive genetic algorithm optimizing neural network (AGA-BP) applied in the gun-launched ground vibratiuncle sensor recognition was introduced. First, as for the BP neural network may not converge to the global minima, an optimization algorithm formed by combination of adaptive genetic algorithm with the BP neural network was proposed and the network was trained by the back-propagation method. Then the simulation was conducted for time-frequency analysis on sampling signals of tracked vehicle and wheeled vehicle, the eigenvalue was extracted from sampled signals by wavelet transform. The sample training and recognition were performed by optimized algorithm and traditional algorithm, the comparison shows recognition error can be reduced by the method.