USING NEURAL NETWORKS PROGRAMMING ON JAVA FOR SOLVING THE PROBLEM OF SIGNAL RECOGNITION
Ключові слова:
composite materials, neural networks, multilayer perceptron with back-propagation training, defect, function of activity.Анотація
The results of the study of signal recognition using neural networks are presented. Scanning of composite materials is performed in the presence of additive noise. A multilayer perceptron with back-propagation error is implemented on Java in the environment NetBeans. Experiments were performed to analyze MSE values and accuracy. The confusion matrix, sensitivity and specificity were obtained and analyzed.
Посилання
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5. Handetskiy V.S. Spektralnaya identifikatsiya signalov v defektoskopii kompozitov s ispolzovaniem teorii statisticheskih ispyitaniy / Handetskiy V.S., Gerasimov V.V. //VIsnik DNU: FIzika. RadIoelektronIka. – DnIpropetrovsk; 2003. №10 – S.128-132.
6. Matveeva N.A. Modelirovanie neyroseti dlya resheniya zadachi klassifikatsii v defektoskopii // SistemnI tehnologIYi. RegIon. mIzhvuz. zb. nauk. prats. - DnIpropetrovsk: «SistemnI tehnologIYi», 2011. -Vip. 1(72). - S. 37-44.
2. Haykin S. Neural Networks. A Comprehensive Foundation. Second edition. – New Jersey: Prentice Hall, 2008. -1103 p.
3. C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
4. Herbert Schildt Java.The Complete Reference Ninth edition, 2014, 1372p.
5. Handetskiy V.S. Spektralnaya identifikatsiya signalov v defektoskopii kompozitov s ispolzovaniem teorii statisticheskih ispyitaniy / Handetskiy V.S., Gerasimov V.V. //VIsnik DNU: FIzika. RadIoelektronIka. – DnIpropetrovsk; 2003. №10 – S.128-132.
6. Matveeva N.A. Modelirovanie neyroseti dlya resheniya zadachi klassifikatsii v defektoskopii // SistemnI tehnologIYi. RegIon. mIzhvuz. zb. nauk. prats. - DnIpropetrovsk: «SistemnI tehnologIYi», 2011. -Vip. 1(72). - S. 37-44.
Завантаження
Опубліковано
2019-01-01
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