摘要
为了解决传统接触式煤位检测方法存在的缺点,如使用寿命短、时间长,费工费时等。提出了基于细胞神经网络的煤仓图像处理方法,核心思想是:通过CNN基本状态方程的基本原理,构造出数学形态滤波的细胞神经网络MMCNN方程。细胞神经网络完成了膨胀、腐蚀具体的计算。经过测试验证,效果很好。其应用可以实现远程监控,实时性很好,具有广阔的应用空间。
In order to solve the traditional coal-contact level detection method shortcomings, such as short life, long time, work time-consuming and more. This paper presents a cellular neural networks based image processing method bunker, the core idea of this method is that by CNN basic fundamental equation of state is constructed mathematical morphology filtering cellular neural networks MMCNN equation. Cellular neural networks completed the expansion, corrosion specific calculation. After testing can be seen, the effect is very good. Application of this method can achieve remote monitoring, and real-time performance is very good, with a broad application space.
出处
《煤矿机械》
北大核心
2014年第5期185-186,共2页
Coal Mine Machinery
关键词
细胞神经网络
数学形态
煤仓图像处理
cellular neural networks
mathematical morphology
image processing coal bunker
