Work

Hybrid Neural Network Identifying Complex Dynamic Objects: Comprehensive Modeling and Training Method Modification

Year: 2024

Type: conference-paper

Abstract: The article to the development of mathematical models of complex dynamic objects (using the example of helicopter turboshaft engines) in the form of recurrent neural networks and their use in complex modelling to identify the parameters of automatic control, monitoring, and diagnostic systems is described. For the first time, a concept has been created for constructing neural network models of complex dynamic objects, in which, by increasing the robustness of the functioning of a trained neural network, it becomes possible at its output to increase the reliability of solving problems of identifying complex dynamic objects. The use of a hybrid neural network NARX with a radial-basis nonlinear layer is proposed. The introduction of a radial basis nonlinear layer into the NARX neural network is an effective addition when working with unstructured data such as images, audio signals, or text due to its ability to extract and represent complex patterns and features in these data. This is confirmed by the results of modelling the losses of the neural network, which turned out to be stable over 500 epochs of its training and did not exceed 0.025 (2.5 %). A comprehensive modification of the Levenberg-Marquardt training method is proposed, which consists of a particular application of the Broyden method for calculating the elements of the Hessian matrix, as well as an analytical description of the regularization parameter through the use of control coefficients for increasing or decreasing its value in the event of a neural network training error. The use of the modified Levenberg-Marquardt method made it possible to reduce the average training error of the NARX hybrid neural network with a radial basis layer by 33 % to the level of 0.025.

Source: International Workshop on Computer Modeling and Intelligent Systems (CMIS)

DOI: 10.55056/ceur-ws.org/vol-3702/paper11.pdf

Authors: Victoria Vysotska, Serhii Vladov, Ruslan Yakovliev, Alexey Yurko

Institutions: Lviv Polytechnic National University, Kharkiv National University of Internal Affairs, Kremenchuk Mykhailo Ostrohradskyi National University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W7166308579

FWCI:

Cites: 53

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Related to: 10