基于形态学理论的自动目标识别技术——自适应BP学习算法

Automatic Target Recognition Technique Based on Morphological Theory:Adaptive BP Learning Algorithm

在设计了一种具有实用意义的形态学开、闭滤波的神经网络模型基础上 ,完成了用于目标检测识别的优化学习算法 ,为克服BP算法存在的收敛速度慢、需要选择学习参数且无法保证全局最优等固有缺陷 ,将启发引导策略与学习规则相结合 ,采用了一种动态调控学习参数的自适应BP学习算法。试验结果表明 ,该算法不仅能适应复杂多变的背景环境 ,而且对运动目标的持续检测能力具有位移不变。

Learning aims to emphasize effective stimulation of plastic structure and to form steady memory mode.Data_based machine leaning is an important avenue to make information system attract external information.It is a key technique in field of artificial intelligence.Based on a practical neural network model of filtering for opening and closing,an optimal training algorithm for image target detection is proposed.By means of combining guidable method with the training rule,an adaptive BP training algorithm to adjust learning parameter dynamically is adopted.Experimental results show that the algorithm has invariant property with respect to shift,scale and rotation of moving target in continuing detection of moving targets.