摘要
针对两条虚拟检测线特征提取中人工调节阈值设置的缺陷,为了保证系统能长期在无人值守的状态下高精度稳定运行,提出了基于BP神经网络的阈值自适应技术以解决阈值选取的问题.利用BP网络工作信号正向传播、误差信号反向传播特性,自动修正各层连接权值,最终产生误差极小的输出.实验证明,该算法可以在实际应用环境中获得较为适合的阈值.
Virtual test line for two feature extraction threshold is set manually adjust the defect, in order to ensure long-term system in unattended mode high-precision and stable operation, proposed a BP neural network based adaptive threshold technology to solve the threshold selected issues. The use of forward propagation BP network operating signal, error signal back-propagation characteristics, levels of connection weights automatically correct, the resulting error is very small output. Experimental results show that the algorithm can be obtained in the actual application environment more appropriate threshold.
出处
《微电子学与计算机》
CSCD
北大核心
2012年第8期175-178,184,共5页
Microelectronics & Computer
关键词
BP神经网络
阈值
客流量统计
自适应算法
BP neural network
threshold
customer-counting
adaptive algorithm
