A New Response Approximation Model of the Quadrant Detector Using the Optimized BP Neural Network

Abstract

In this paper, a new response approximation model for quadrant detector is proposed based on a BP Neural Network by employing different training algorithms. A total of 1001 data points are gathered to train and test the proposed network. Through optimal configuration, the network with 1 hidden layer and 8 hidden neurons with Log-sigmoid transfer functions in the hidden layer is determined to have the optimum performance. Furthermore, Levenberg-Marquardt (LM) is the best train algorithm while in this case the model is more precise than others. Besides, it shows a good ability to suppress the non-uniformity. The results of experiment reveal that the root mean square error using this model is about 1/5 of that using Fusion method when the beam radius is ${0.75} \textit {mm} $ . Meanwhile, its maximum errors under different radii are all less than ${6}\times {10}^{\text {-3}} \textit {mm} $ . Therefore, the new model would have a good application prospect in beam position measurements.