Serhii Vladov; Ruslan Yakovliev; Victoria Vysotska; Dmytro Uhryn; Artem Karachevtsev

Polymorphic Radial Basis Functions Neural Network

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Author(s)

Serhii Vladov ¹ Ruslan Yakovliev ² Victoria Vysotska ³ Dmytro Uhryn ^4,* Artem Karachevtsev ⁴

1. Scientific Work Organization and Gender Issues Department, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, Kremenchuk, 39605, Ukraine

2. Air Navigation Department, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, Kremenchuk, 39605, Ukraine

3. Information Systems and Networks Department, Lviv Polytechnic National University, Lviv, 79013, Ukraine

4. Department of Computer Science of the Yuriy Fedkovych Chernivtsi National University, Chernivtsi, 58012, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2024.04.01

Received: 12 Oct. 2023 / Revised: 14 Jan. 2024 / Accepted: 7 Mar. 2024 / Published: 8 Aug. 2024

Index Terms

Polymorphic RBF Network, Training, Multi-criteria Optimization, Pareto Front, Radial Basis Functions

Abstract

The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based on the training classical RBF networks evolutionary algorithm, the polymorphic RBF network training algorithm was created, which, through the initializing weight coefficients methods use taking into account the tasks structure and preliminary values, using the mutations tournament selection, adding additional criteria to the fitness function to take into account stability and speed training a polymorphic RBF network, as well as using an evolutionary mutation strategy, allowed us to obtain the lowest errors in training and testing a polymorphic RBF network compared to known RBF network architectures. The created polymorphic RBF network practical application possibility is demonstrated experimentally using the helicopters turboshaft engines (using the example, the TV3-117 turboshaft engine) operating process parameters optimizing solving task using a multicriteria optimization algorithm. The optimal Pareto front was obtained, which made it possible to obtain the engine operation three additional modes: maximum reduction of specific fuel consumption at the total pressure in the compressor increase degree increased value by 5.0 %, specific fuel consumption minimization at the total pressure in the compressor increase degree reduced value by 1.0 %, the total pressure in the compressor increases degree optimal value with a slight increase in specific fuel consumption by 10.5 %. Future research prospects include adapting the developed methods and models into the general concept for monitoring and controlling helicopter turboshaft engines during flight operations. This concept is implemented in the neural network expert system and the on-board automatic control system.

Cite This Paper

Serhii Vladov, Ruslan Yakovliev, Victoria Vysotska, Dmytro Uhryn, Artem Karachevtsev, "Polymorphic Radial Basis Functions Neural Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.4, pp.1-21, 2024. DOI:10.5815/ijisa.2024.04.01

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