Interpretation function of dynamic of an underwater vehicle in non-stationary environment

被引:1
|
作者
Nechaev, Yuri, I [1 ]
Nikushchenko, Dmitry, V [1 ]
机构
[1] St Petersburg State Marine Tech Univ, St Petersburg, Russia
来源
关键词
interpretation function; non-stationary dynamic; unmanned underwater object; evolutionary environment; modern catastrophe theory; urgent computing;
D O I
10.37220/MIT.2022.56.2.055
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The construction and analysis of the interpretation functions of the models of unsteady dynamics of new generation an underwater vehicle (UV) based on the modern theory of disasters are considered. The formal apparatus of conceptual solutions and principles of constructing interpretation functions is implemented in a non-stationary dynamic environment within the framework of the principle of competition. The procedures of the interpretation functions are based on the use of various interaction models depending on the level of acting disturbances. The uncertainty and incompleteness of the initial information on the dynamics of the interaction of underwater vehicles in a non-stationary environment determined the approach to constructing interpretation functions when constructing a mathematical description of the problems of non-stationary dynamics of underwater vehicles based on the concept of soft computing and the identification of "hidden" knowledge. The developed models and algorithms for interpreting unsteady dynamics of submarines are implemented in the functional block for modeling a multifunctional software complex for dynamic visualization of unsteady dynamics of underwater vehicles in emergency computing mode Urgent Computing.
引用
下载
收藏
页码:139 / 145
页数:7
相关论文
共 50 条
  • [21] Dynamic memory model for non-stationary optimization
    Bendtsen, CN
    Krink, T
    CEC'02: PROCEEDINGS OF THE 2002 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2002, : 145 - 150
  • [23] Learning non-stationary dynamic bayesian networks
    Robinson, Joshua W.
    Hartemink, Alexander J.
    Journal of Machine Learning Research, 2010, 11 : 3647 - 3680
  • [24] Learning Non-Stationary Dynamic Bayesian Networks
    Robinson, Joshua W.
    Hartemink, Alexander J.
    JOURNAL OF MACHINE LEARNING RESEARCH, 2010, 11 : 3647 - 3680
  • [25] Dynamic Adaptation on Non-stationary Visual Domains
    Shkodrani, Sindi
    Hofmann, Michael
    Gavves, Efstratios
    COMPUTER VISION - ECCV 2018 WORKSHOPS, PT II, 2019, 11130 : 158 - 171
  • [26] Stationary and non-stationary probability density function for non-linear oscillators
    Muscolino, G
    Ricciardi, G
    Vasta, M
    INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS, 1997, 32 (06) : 1051 - 1064
  • [27] The Parzen Kernel Approach to Learning in Non-stationary Environment
    Pietruczuk, Lena
    Rutkowski, Leszek
    Jaworski, Maciej
    Duda, Piotr
    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 3319 - 3323
  • [28] Maximizing Reward in a Non-Stationary Mobile Robot Environment
    Dani Goldberg
    Maja J. Matarić
    Autonomous Agents and Multi-Agent Systems, 2003, 6 : 287 - 316
  • [29] Voice driven applications in non-stationary and chaotic environment
    Kwan, C.
    Li, X.
    Lao, D.
    Deng, Y.
    Ren, Z.
    Raj, B.
    Singh, R.
    Stern, R.
    2005 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, 2006, : 127 - +
  • [30] A Policy Search and Transfer Approach in the Non-stationary Environment
    Zhu F.
    Liu Q.
    Fu Q.-M.
    Chen D.-H.
    Wang H.
    Fu Y.-C.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2017, 45 (02): : 257 - 266