A radial basis neural network based utility array detector spot positioning method
By using a radial basis function neural network optimization scheme, the problems of high computational load and low accuracy in spot positioning of array detectors are solved, achieving high-precision spot positioning with low computational load, which is suitable for multi-module array detectors.
CN116182707BActive Publication Date: 2026-07-03OCEAN UNIV OF CHINA
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- OCEAN UNIV OF CHINA
- Filing Date
- 2023-03-06
- Publication Date
- 2026-07-03
AI Technical Summary
Technical Problem
In existing technologies, the spot localization method for array detectors has a large computational load, low accuracy, and is not suitable for array detectors. Feedforward neural networks have many parameters and limited fitting ability.
Method used
A radial basis function neural network (RBN) is used for spot localization. The RBN is designed and trained, and the Gaussian function is used as the function of the hidden layer nodes to optimize the neural network structure and parameters. The spot position is predicted by combining the training set and the test set.
Benefits of technology
It achieves high-precision spot positioning, reduces computational load, is suitable for n*n multi-module array detectors, and is simple to operate and highly accurate.
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Figure CN116182707B_ABST
Abstract
This invention relates to a practical laser spot localization method for array detectors based on radial basis function (RBF) neural networks, belonging to the field of detector technology. It utilizes RBF neural networks to achieve high-precision localization of laser spots. The RBF neural network is designed and trained, using the training set as the input layer and the output layer as the predicted spot position. The parameters and number of neurons are continuously trained and adjusted. The test set is input into the trained neural network to predict the spot position, and the effectiveness of the RBF neural network is tested. This invention has fewer parameters, lower computational load, higher accuracy, and is applicable to n*n multi-module array detectors. Furthermore, the experimental acquisition process in this application uses a quadrant detector, making the operation relatively simple.
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