CCD光电响应曲线的神经动力学拟合

针对在CCD 输出灰度值较小的区域，最小二乘法拟合的曲线与实测值之间容易出现偏大的相对误差，本文提出以最大相对误差最小化为评价指标，使用神经动力学寻优求解曲线拟合值的方法，获得最优拟合。首先以大功率LED 为线性可调光源测量CCD 的光电响应数据，并设计多项式拟合的光电测量的误差模型，然后根据相对误差最小化的评价指标，采用神经动力学优化算法求解最大相对误差最小的拟合曲线。实验结果表明，寻优算法稳定有效，当多项式拟合阶次N=3 时，拟合的响应曲线的最大相对误差为2.5%，明显优于最小二乘法。此外，响应数据分析表明，CCD ICX694AL 在未饱和时的光电响应为线性关系，但在饱和区域，光电响应的非线性明显。

Abstract

The large relative error between the measurements and the fitted values with the least squares method easily occurred when the small outputs of CCD. According to this problem, the evaluation method with minimizing the value of the maximum relative error was proposed to solve the optimum curve fitting with the neural dynamic optimization algorithm. Firstly, the CCD opto-electronic responses were achieved by using the high power LED as a linear light source, and the polynomial fitting error model was advanced. Then the neural dynamic optimization algorithm was used to solve it. The experimental results show that the optimum algorithm is stable and effective, and the maximum relative error is not more than 2.5% in the third order polynomial fitting with the neural dynamics. The relative error is obviously reduced in comparison with the least square method. By analyzing the responses, the CCD ICX694AL is with linear characteristics in the unsaturated region. However, it has obvious nonlinear effects in the saturated region.

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CCD光电响应曲线的神经动力学拟合

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