Background Light Elimination of One-Dimension Position Sensitive Detector ON Sub-block Integration Neural Network Optimum Method
The one -dimension position sensitive detector(PSD) is photo-electronic sensor which can detect the position of a light spot traveling over its surface, and convert the position of light spot to simple electric current signal. Based upon one -dimension PSD, many types of precision and contactless motion detection instruments could be constructed. The most important problem to use the one -dimension PSD is how to overcome the influence of background light action on the PSD, therefore to improve the precision and reliable of the instrument. This paper following the principle and feature equations of the one -dimension PSD analyses background light. It is found that one -dimension PSD is nonlinear when there exist background light. A method for non-linearity compensation of background light voltage based on neural network high accuracy linearization sub-block network integration interpolation is presented in this paper. In order to background light compensation over a full range, the neural network is trained to properly represent the nonlinear mapping between sensor reading and their represent output accurately at different background light. It is revealed from the computer experiment result that the influence of background light fluctuation can be eliminated effectively, and a desired linear relationship between the sensor input and the neural network output can be obtained.
one-dimension position sensitive detector background light non-1inearity sub-block integration nerve network
Mo Changtao Du Xin Zhang Li-li Wang Ming
College of Foundation Science, Harbin University of Commerce,Harbin 150028, China
国际会议
第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)
重庆
英文
2181-2185
2009-08-01（万方平台首次上网日期，不代表论文的发表时间）