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提取干涉条纹的中心是干涉测量的关键环节,文中提出了一种基于细胞神经网络(CNN)提取干涉条纹中心的新方法。CNN是一种实时处理信号的大规模非线性模拟电路,同时它的局部联接特点使其适用于超大规模集成电路的实现。CNN具有并行运算的能力,可消除传统串行算法复杂性高、不能实时处理的缺点。对该方法进行了分析,给出了实例的仿真结果,证明该方法能快速准确地提取干涉条纹的中心,提高了干涉条纹的判别精度,从而增加了实验中干涉条纹处理的直观性和实时性。
Extracting the center of interference fringes is a key step in interferometry. In this paper, a new method for extracting interference fringe centers based on cellular neural network (CNN) is proposed. CNN is a kind of large-scale non-linear analog signal processing signal in real time, and its local connection characteristics make it suitable for VLSI implementation. CNN has the ability of parallel computing, which can eliminate the disadvantages of traditional serial algorithms, which are complex and can not be processed in real time. The method is analyzed and the simulation results are given. It is proved that the method can extract the center of the interference fringe quickly and accurately and improve the discrimination accuracy of the interference fringes, which increases the visualization and real-time performance of the fringe processing .