基于基因算法的激光干涉计量精度的提高

提出一种基于改进的基因算法的提高激光干涉CCD计量精度的数据处理方法，并将该方法与传统基因算法作余弦曲线拟合以及最小二乘法作二次曲线拟合进行比较。结果表明，用改进的基因算法优化，不但解决了CCD光学干涉计量中像素尺寸以及图像采集卡空间量化误差对测量精度的影响，而且解决了目标函数多极值问题，从而干涉条纹可获得高于光敏像素级定位精度。最后将干涉条纹的光强精确地拟合出来，讨论曲线拟合误差，表明该算法具有很好的鲁棒性和自适应能力。

Improvement of the Interferometric Precision of Laser Beams Based on Genetic Algorithms

A novel data processing method to improve the interference metric precision of laser beams based on improved genetic algorithms (GAs) is presented. The improved GAs to fit curves is compared with the conventional GAs and the conventional least square method to fit curves. Using the improved genetic algorithms, the results show that the influence on the precision of measurement from errors in quantitating the size of image element of CCD and on the image-gathering cards in CCD optic interference measurement is removed, and that the multi-extremum of object function is resolved. The location precision of interference stripes is also increased, and the intensity distribution of the stripes is fitted accurately. The error of curve fitting is also discussed . GAs is shown to be very well in possession of robustness and adaptability.