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FIXED-BUDGET APPROXIMATION OF THE INVERSE KERNEL MATRIX FOR IDENTIFICATION OF NONLINEAR DYNAMIC PROCESSES

Описание

Тип публикации: статья из журнала

Год издания: 2022

Идентификатор DOI: 10.5937/jaes0-31772

Ключевые слова: computational efficiency, identification, kernel methods, low-rank approximation, nonlinear process

Аннотация: The paper considers the identification of nonlinear dynamic processes using kernel algorithms. Kernel algorithms rely on a nonlinear transformation of the input data points into a high-dimensional space that allows solving nonlinear problems through the construction of kernelized counterparts of linear methods by replacing the inneПоказать полностьюr products with kernels. A key feature of the kernel algorithms is high complexity of the inverse kernel matrix calculation. Nowadays, there are two approaches to this problem. The first one is based on using a reduced training data sample instead of a full one. In case of kernel methods, this approach could cause model misspecification, since kernel methods are directly based on training data. The second one is based on the reduced-rank approximations of the kernel matrix. A major limitation of this approach is that the rank of the approximation is either unknown until approximation is done or it is predefined by the user, both of which are not efficient enough. In this paper, we propose a new regularized kernel least squares algorithm based on the fixed-budget approximation of the kernel matrix. The proposed algorithm allows regulating the computational burden of the identification algorithm and obtaining the least approximation error. We have shown some simulations results illustrating the efficiency of the proposed algorithm compared to other algorithms. The application of the proposed algorithm is considered on the identification problem of the input and output pressure of the pump station. © 2022 Institut za Istrazivanja. All rights reserved.

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Издание

Журнал: Journal of Applied Engineering Science

Выпуск журнала: Vol. 20, Is. 1

Номера страниц: 150-159

ISSN журнала: 14514117

Издатель: Institute for Educational Research

Персоны

  • Antropov N. (Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, Department of Systems Analysis and Operations Research, Krasnoyarsk, Russian Federation)
  • Agafonov E. (Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, Department of Systems Analysis and Operations Research, Krasnoyarsk, Russian Federation, Siberian Federal University, School of Petroleum and Natural Gas Engineering, Department of Fuel Supply and Combustibles, Krasnoyarsk, Russian Federation)
  • Tynchenko V. (Siberian Federal University, School of Petroleum and Natural Gas Engineering, Department of Technological Machines and Equipment of Oil and Gas Complex, Krasnoyarsk, Russian Federation, Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, Information-Control Systems Department, Krasnoyarsk, Russian Federation)
  • Bukhtoyarov V. (Siberian Federal University, School of Petroleum and Natural Gas Engineering, Department of Technological Machines and Equipment of Oil and Gas Complex, Krasnoyarsk, Russian Federation, Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, Department of Information Technology Security, Krasnoyarsk, Russian Federation)
  • Kukartsev V. (Siberian Federal University, Institute of Space and Information Technologies, Department of Computer Science, Krasnoyarsk, Russian Federation, Reshetnev Siberian State University of Science and Technology, Engineering and Economics Institute, Department of Information Economic Systems, Krasnoyarsk, Russian Federation)

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