A beam pattern evaluation method using neural network has been developed to assist non-expert cyclotron operators.
While an expert operator can easily tell beam accelerating conditions by the beam pattern measured by a scanned beam probe, it is not easy for non-expert operators to evaluate the pattern.
The followings are the summarized procedure of the proposed method.
First, the features of the beam patterns, which correspond to the view points of the experts, are extracted using Gabor expansion. A neural network algorithm is applied to calculate the Gabor expansion.
Next, the number of the extracted features is reduced by averaging the features of high frequency ranges in five partial zones. The idea of this process is based on the fact that the operators do not pay attention to the details of the high frequency components of the patterns.
Finally, the pattern evaluation process by the expert operators is learned by the back-propagation algorithm on a multi-layered feed forward neural network.
Parallel processing architecture of the feature extraction network, and the learning capability of the non-linear clustering network are very useful for the evaluation model of beam patterns.
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