The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@InProceedings{sun:napac2019-weple07,
author = {Y.P. Sun},
title = {{Transfer Matrix Classification with Artificial Neural Network}},
booktitle = {Proc. NAPAC'19},
pages = {898--900},
paper = {WEPLE07},
language = {english},
keywords = {network, quadrupole, dipole, framework, software},
venue = {Lansing, MI, USA},
series = {North American Particle Accelerator Conference},
number = {4},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2019},
issn = {2673-7000},
isbn = {978-3-95450-223-3},
doi = {10.18429/JACoW-NAPAC2019-WEPLE07},
url = {http://jacow.org/napac2019/papers/weple07.pdf},
note = {https://doi.org/10.18429/JACoW-NAPAC2019-WEPLE07},
abstract = {Standard neural network algorithms are developed for classification and regression applications. In this paper, the details of the neural network algorithms are presented, together with several applications. Artificial neural network is trained to classify multi-class transfer matrix of different types of particle accelerator components. It is shown that with a fully-connected feedforward neural network, it is possible to get high accuracy of 99% on training data, validation data and test data.},
}