533.6 Modeling a neural network to solve the problem of classifying air frame elements

Bulgakov V. N. (Bauman Moscow State Technical University), Raclav R. A. (Bauman Moscow State Technical University), Sapozhnikov D. A. (МГТУ им.Н.Э.Баумана), Chernyshev I. V. (Bauman Moscow State Technical University)


doi: 10.18698/2309-3684-2018-4-5771

The paper introduces a neural network implemented to classify air frame surface ele-ments. Within the research, we generated a sample containing the surface parameters of classification objects. In order to avoid errors associated with different measurement scales, the criteria were scaled. According to synthetic data, the neural network was trained, and the proposed model was verified. The optimal configuration of the neural network was determined experimentally. As a criterion of optimality, we used the proportion of correct answers from the test and training samples, and carried out calibration and modification of individual model parameters. The classification results of the test sample by the optimal network were summarized in the error matrix. The most significant result was achieved when distinguishing the class of ellipsoids. Separate blocks of the matrix show that the neural network accurately distinguishes the classes of ellipsoids and hyperboloids. The study proposes some ideas for further modification of the algorithm in order to increase the proportion of correct answers when distinguishing the class of paraboloids.

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