In the research, based on BP neural network theory, a printer calibration model is provided according to sorting experiment data by hue Angle range.
在研究中，利用BP神经网络理论，提出并建立了按色相角范围对实验数据分类的打印机标定模型。
The work principle of the bionic polarized navigation sensor was analyzed, and then the sensor experiment model and optic calibration instrument were set up.
针对研制仿生导航微传感器的需要，设计并且搭建了偏振导航传感器实验模型。
The experiment results indicate that the improved BP network model can avoid nonlinear modeling and enhance the calibration precision and the flexibility, which has real significance.
结果表明：采用改进的BP神经网络能够避免对摄像机进行非线性建模，有利于提高标定精度，增加系统的灵活性，更具有实际意义。
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