Abstract
The research results of signal recognition using neural networks are presented. A multilayer perceptron with back-propagation error is implemented on Java. The optimal number of neurons in the hidden layer is selected for building an effective architecture of the neural network. Training network on different sets of signals with noise allowed teaching her to work with distorted information, which is typical for non-destructive testing in real conditions. Experiments were performed to analyze MSE values and accuracy.
Highlights
The research results of signal recognition using neural networks are presented
The purpose of this work is to create a neural network for the classification of electromagnetic signals that are obtained by scanning the composite material, as well as for solving problems of defect detection
Each artificial neural network is a set of simple elements neurons that are connected in some way
Summary
The research results of signal recognition using neural networks are presented. A multilayer perceptron with back-propagation error is implemented on Java. «Системні технології» 1 (126) 2020 «System technologies» Modern technologies allow to create computer systems with involving neural networks [1] for whom the as input parameters can be used characteristics of electromagnetic signals. The purpose of this work is to create a neural network for the classification of electromagnetic signals that are obtained by scanning the composite material, as well as for solving problems of defect detection.
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