三种光电混合神经网络系统衍射误差的仿真研究

介绍了三种类型的光电混合神经网络系统,重点研究了衍射引起的误差对系统输出的影响。根据衍射、光学信息处理和神经网络理论,采用实验中的参量和输入数据,对衍射造成的输出误差做了仿真分析。分析表明,近场衍射造成较大的输出误差。近场与远场衍射综合作用时,误差因输入图像模式不同而差异较大;其中,当输入较小的简单图像时相对误差较大;而对实验中实际采用的复杂图像,相对误差较小。利用线性回归方法对输出数据做了校正,并分析了其可行性,校正后的数据误差降低一个量级。衍射误差对实验中识别率的影响可以控制在较小的范围内,识别率可以保持在97.7%以上。

Simulation on the Errors of Three Opto-Electronic Hybrid Neural Networks Induced by Diffraction

Three types of opto-electronic hybrid neural networks are introduced. The errors induced by diffraction in these networks are emphasized and analyzed with computer simulation using the experimental parameters and input data, where diffraction, optical information processing and neural network theories are applied. It shows near-field diffraction induces large magnitudes of relative errors. When near-field diffraction and far-field diffraction are employed together, the errors are different according to the display modes. When the display mode is a small image, the magnitudes of relative errors are very large. However, when the display mode is a complicated image, the magnitudes of relative errors are small. The feasibility of using linear regression to calibrate the output data is discussed. It is found that linear regression can reduce the errors for about one magnitude. According to the analyses, the errors induced by diffraction can be minimized to a low level in the experiments, and thereby, the recognition rates can be maintained at a high level (larger than 97.7%).