Structural Dynamics Optimization of Gun Based on Neural Networks and Genetic Algorithms

LIANG Chuan-jian;YANG Guo-lai;WANG Xiao-feng

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2015, Vol. 36 ›› Issue (5) : 789-794. DOI: 10.3969/j.issn.1000-1093.2015.05.004
Paper

Structural Dynamics Optimization of Gun Based on Neural Networks and Genetic Algorithms

  • LIANG Chuan-jian, YANG Guo-lai, WANG Xiao-feng
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Abstract

In order to study the optimization of muzzle disturbance, a new method of gun structural dynamics optimization based on nonlinear finite element method, experimental design, neural networks and genetic algorithms is proposed. A dynamic model of a large caliber gun is established based on the nonlinear finite element method, and the structural dynamics analysis of the gun is made based on experimental design. With experimental data as training samples, a back-propagation (BP) neural network is established to simulate the nonlinear mapping between the structural parameters and muzzle disturbance index based on Bayesian regularization algorithm. The optimal objective function of muzzle disturbance is constructed, the genetic algorithms is applied to solve the objective function, and the optimal design for structural parameters of the gun is realized. The results show that nonlinear relationship between the structural parameters and muzzle disturbance index established by the method is proved to be highly reliable, and the method is accurate and feasible to optimize the muzzle disturbance.

Key words

ordnance science and technology / nonlinear finite element / experimental design / neural networks / structural dynamics optimization

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LIANG Chuan-jian, YANG Guo-lai, WANG Xiao-feng. Structural Dynamics Optimization of Gun Based on Neural Networks and Genetic Algorithms. Acta Armamentarii. 2015, 36(5): 789-794 https://doi.org/10.3969/j.issn.1000-1093.2015.05.004

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