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Journal articles on the topic 'Method of identification systems'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

LYSENKO, S., V. OMELIANENKO, and R. SHCHUKA. "METHOD FOR THE SPYWARE IDENTIFICATION IN THE COMPUTER SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 285, no. 3 (2020): 43–49. https://doi.org/10.31891/2307-5732-2020-285-3-7.

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The paper presents a method of identification of spyware identification software in computer systems, which allows detection of all types, and differs from the known ones, which provides the principle of proactivity and is based on mechanisms of machine learning. For the proposed method of identifying spyware, it uses an analysis of the behaviour of the software on computer systems. The method is also based on a downstream analysis of the involvement of machine learning apparatus, in particular reinforcement learning, which operates with concepts of stability values for the malware detection.

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2

Volkova, Natalya P., and Viktor N. Krylov. "HYBRID TEXTURE IDENTIFICATION METHOD." Herald of Advanced Information Technology 4, no. 2 (2021): 123–34. http://dx.doi.org/10.15276/hait.02.2021.2.

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The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures - spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regi

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3

Gorobets, Alexander. "STRUCTURAL IDENTIFICATION METHOD OF SIMULTANEOUS EQUATIONS SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 191–94. http://dx.doi.org/10.3182/20020721-6-es-1901.01422.

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4

Galetta, Clement, Jean-Marc Roussel, and Jean-Marc Faure. "A relative identification method for reactive systems." IFAC-PapersOnLine 51, no. 7 (2018): 152–59. http://dx.doi.org/10.1016/j.ifacol.2018.06.294.

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5

Lyashevskiy, Sergey, Larry A. Abel, Sergey Lyashevskiy, and Larry A. Abel. "Nonlinear Systems Identification using the Lyapunov Method." IFAC Proceedings Volumes 27, no. 8 (1994): 271–76. http://dx.doi.org/10.1016/s1474-6670(17)47727-5.

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6

Niven, Robert, Ali Mohammad-Djafari, Laurent Cordier, Markus Abel, and Markus Quade. "Bayesian Identification of Dynamical Systems." Proceedings 33, no. 1 (2020): 33. http://dx.doi.org/10.3390/proceedings2019033033.

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Many inference problems relate to a dynamical system, as represented by dx/dt = f (x), where x ∈ ℝn is the state vector and f is the (in general nonlinear) system function or model. Since the time of Newton, researchers have pondered the problem of system identification: how should the user accurately and efficiently identify the model f – including its functional family or parameter values – from discrete time-series data? For linear models, many methods are available including linear regression, the Kalman filter and autoregressive moving averages. For nonlinear models, an assortment of mach

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7

Zhou, Hao, Wen Zuo, Yaojun Qiao, et al. "Ultrasonic phase extraction method for co-cable identification in coherent optical transmission systems." Chinese Optics Letters 22, no. 10 (2024): 100601. http://dx.doi.org/10.3788/col202422.100601.

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8

Sawaragi, Hiroshi, Jingwu Yao, and Sueo Sugimoto. "Identification of Multivariable State Feedback Systems Using the Indirect Identification method." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1998 (May 5, 1998): 99–104. http://dx.doi.org/10.5687/sss.1998.99.

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9

Zuev, Alexander, Alexey Zhirabok, Vladimir Filaretov, and Alexander Protsenko. "Method of faults identification in non-stationary systems." Robotics and Technical Cybernetics 10, no. 1 (2022): 55–63. http://dx.doi.org/10.31776/rtcj.10106.

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The article presents a new method for faults identification in mechatronic systems, described by non-stationary nonlinear differential equations, in the presence of perturbations. Observers working in a sliding mode are used to solve the problem. The proposed approach is based on the idea of constructing a reduced (with lower dimension) model of the original system with selective sensitivity to faults and disturbances. The main purpose of introducing such a model is the ability to take into account the nonstationarity of the system. The stated theory is illustrated by a practical example. Key

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10

Janot, A. "Using the SDP identification method for electromechanical systems." IFAC-PapersOnLine 54, no. 7 (2021): 809–14. http://dx.doi.org/10.1016/j.ifacol.2021.08.461.

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11

Negoita, Gh M., and V. V. Canciu. "Fuzzy Identification of Systems: A New Cybernetic Method." Kybernetes 21, no. 4 (1992): 67–79. http://dx.doi.org/10.1108/eb005936.

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12

Brenner, Marty, and Yuesheng Xu. "A factorization method for identification of Volterra systems." Journal of Computational and Applied Mathematics 144, no. 1-2 (2002): 105–17. http://dx.doi.org/10.1016/s0377-0427(01)00553-2.

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13

Ligarski, Mariusz J. "Problem identification method in certified quality management systems." Quality & Quantity 46, no. 1 (2010): 315–21. http://dx.doi.org/10.1007/s11135-010-9356-2.

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14

Liu, G. P., and H. Wang. "An Improved Recursive Identification Method For Bilinear Systems." International Journal of Modelling and Simulation 13, no. 2 (1993): 88–91. http://dx.doi.org/10.1080/02286203.1993.11760185.

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15

Chernyshov, N. M., and I. K. Romanova-Bolshakova. "OBJECT IDENTIFICATION METHOD FOR INTEGRATION WITH ROBOTIC SYSTEMS." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 2 (April 27, 2025): 140–50. https://doi.org/10.18522/2311-3103-2025-2-140-150.

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16

Xie, Chun Li, Dan Dan Zhao, and Pei Chang Wang. "A Modeling Method of Switched Systems Based on AP Clustering." Applied Mechanics and Materials 556-562 (May 2014): 3567–70. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3567.

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The behavior of hybrid systems is determined by combining continuous variables with discrete switching logic. The identification of a hybrid systems aims to find an accurate model of the system's dynamics based on its past inputs and outputs. For a class of hybrid systems in the switched form, a model identification method based on affinity propagation clustering is presented in this paper. In this method, the model identification issue is equivalent to the problems of classification of the cluster data of the system and regression of the classification data. The clustering algorithm is succes

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17

Saga, Milan, Karel Perutka, Ivan Kuric, et al. "Methods of Pre-Identification of TITO Systems." Applied Sciences 11, no. 15 (2021): 6954. http://dx.doi.org/10.3390/app11156954.

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The content of this article is the presentation of methods used to identify systems before actual control, namely decentralized control of systems with Two Inputs, Two Outputs (TITO) and with two interactions. First, theoretical assumptions and reasons for using these methods are given. Subsequently, two methods for systems identification are described. At the end of this article, these specific methods are presented as the pre-identification of the chosen example. The Introduction part of the paper deals with the description of decentralized control, adaptive control, decentralized control in

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18

TAKENAKA, Yuji, and Shuichi ADACHI. "Identification Inputs Generating Method for MISO Systems by Least-squares Method." Transactions of the Society of Instrument and Control Engineers 47, no. 6 (2011): 291–93. http://dx.doi.org/10.9746/sicetr.47.291.

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19

LYSENKO, S., and V. KOMAROV. "METHOD AND SOFTWARE FOR DOMAIN-FLUX BOTNET IDENTIFICATION." Herald of Khmelnytskyi National University. Technical sciences 285, no. 3 (2020): 34–42. https://doi.org/10.31891/2307-5732-2020-285-3-6.

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The purpose of this paper is to develop a method for detecting domain-flux botnet. In this paper, we focus on detecting domain‐flux botnets based on Domain Name System (DNS) traffic features. We have explored the peculiarities of the domain-flux botnets and developed a botnet model based on DNS, DNS traffic model and model of the detection process determine all features. This method passively captures all DNS traffic from network and then extract all useful data from each DNS message. This method combines handling DNS query failures, the use of frequency domain lexical analysis and the analysi

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20

Svyd, I. V., I. I. Obod, O. S. Maltsev, et al. "Method for increasing noise immunity of radar "friend or foe" identification systems under the action of intentional correlated interference." Radiotekhnika, no. 205 (July 2, 2021): 154–60. http://dx.doi.org/10.30837/rt.2021.2.205.16.

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The paper analyzes the principles of construction and structure of "friend or foe" identification systems. It is revealed, that the party, interested in the existing system, has the ability of unauthorized use of this information resource for long-range determination of air objects coordinates, on the one hand, and distortion of information of this information resource, on the other hand, which leads to unpredictable consequences. It is shown, that the most vulnerable place in the "friend or foe" identification systems is the aircraft transponder, which significantly affects noise stability an

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21

Yang, Junhui, Wei Liu, and Zongtian Liu. "A new method of event relation identification." International Journal of Intelligent Information and Database Systems 12, no. 4 (2019): 335. http://dx.doi.org/10.1504/ijiids.2019.10026241.

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22

Yang, Junhui, Zongtian Liu, and Wei Liu. "A new method of event relation identification." International Journal of Intelligent Information and Database Systems 12, no. 4 (2019): 335. http://dx.doi.org/10.1504/ijiids.2019.104531.

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23

Abdulkader, Zaid A. "Fingerprint Identification System Using Half Smoothing Filters." Webology 19, no. 1 (2022): 406–18. http://dx.doi.org/10.14704/web/v19i1/web19029.

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Fingerprints have been significantly utilized for enormous security systems for their high-level accuracy. They even have been used effectively in smartphone devices for payment in addition to the security purpose. In this paper we propose a new finger print recognition method based on ridge and valley features in finger print images. These features extracted by rotating Gaussian semi _filters. The novelty of this finger print approach based on the mixing of directional filters and differences of Gaussian filter ideas. We obtain a new ridge/valley detector enabling very precise detection of ri

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24

Fan, Zhiping, Zhengyun Ren, Angang Chen, Xue Feng, and Wenbin Wang. "A Method for Parameter Identification of Combined Integrating Systems." Mathematical Problems in Engineering 2020 (February 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/4097429.

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This manuscript presents a novel structure of combined integration in the process industry and proposes an efficient method for identifying its parameters. Combined integrating processes are delay-time processes that widely exist in the industry. Conventional identification methods have a low-identification accuracy, a large vibration amplitude of the identification curve, and a poor effect for this kind of process. In this paper, a new variable forgetting factor recursive least squares method was adopted to ameliorate this problem. The method could quickly track the mutation of the ideal para

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25

Bianchi, Federico, Alessandro Falsone, Luigi Piroddi, and Maria Prandini. "An alternating optimization method for switched linear systems identification." IFAC-PapersOnLine 53, no. 2 (2020): 1071–76. http://dx.doi.org/10.1016/j.ifacol.2020.12.1297.

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26

UCHIDA, Motomiki, Yukihiro TOYOTA, Hideo NAKAMURA, et al. "A Simulator-Based Quick Identification Method of Nonlinear Systems." Transactions of the Society of Instrument and Control Engineers 30, no. 8 (1994): 908–16. http://dx.doi.org/10.9746/sicetr1965.30.908.

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27

NI, Bo-Yi, and De-Yun XIAO. "A Recursive Identification Method for Non-uniformly Sampled Systems." Acta Automatica Sinica 35, no. 12 (2010): 1520–27. http://dx.doi.org/10.3724/sp.j.1004.2009.01520.

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28

Gubarev, Vyacheslav F., and Aleksey O. Zhukov. "Research of Method of Multidimensional Discrete Systems Iterative Identification." Journal of Automation and Information Sciences 40, no. 9 (2008): 20–35. http://dx.doi.org/10.1615/jautomatinfscien.v40.i9.20.

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29

Melnichuk, Sergey V. "Modified Frequency Method of Structural-Parametric Identification of Systems." Journal of Automation and Information Sciences 47, no. 8 (2015): 63–73. http://dx.doi.org/10.1615/jautomatinfscien.v47.i8.60.

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30

BEGHELLI, S., R. P. GUIDORZI, and U. SOVERINI. "Structural identification of muitivariabie systems by the eigenvector method." International Journal of Control 46, no. 2 (1987): 671–78. http://dx.doi.org/10.1080/00207178708933921.

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31

Campi, Marco C., Toshiharu Sugie, and Fumitoshi Sakai. "An Iterative Identification Method for Linear Continuous-Time Systems." IEEE Transactions on Automatic Control 53, no. 7 (2008): 1661–69. http://dx.doi.org/10.1109/tac.2008.929371.

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32

Onodera, Koichi, Genichi Emoto, and S. Joe Qin. "A NEW SUBSPACE IDENTIFICATION METHOD FOR CLOSED-LOOP SYSTEMS." IFAC Proceedings Volumes 39, no. 1 (2006): 1056–61. http://dx.doi.org/10.3182/20060329-3-au-2901.00169.

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33

Dai, H., and N. K. Sinha. "Iterative instrumental variable method for robust identification of systems." IEEE Transactions on Industrial Electronics 42, no. 5 (1995): 480–86. http://dx.doi.org/10.1109/41.464610.

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34

Xu, H., S. Dasgupta, and Z. Ding. "A Novel Channel-Identification Method for Wireless Communication Systems." IEEE Transactions on Communications 52, no. 10 (2004): 1767–76. http://dx.doi.org/10.1109/tcomm.2004.836511.

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35

Dan, Dan-hui, Ye Xia, Bin Xu, Fei Han, and Xing-fei Yan. "Multistep and Multiparameter Identification Method for Bridge Cable Systems." Journal of Bridge Engineering 23, no. 1 (2018): 04017111. http://dx.doi.org/10.1061/(asce)be.1943-5592.0001145.

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36

Ohsmann, M. "An iterative method for the identification of MA systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 36, no. 1 (1988): 106–9. http://dx.doi.org/10.1109/29.1496.

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37

Rajapandiyan, C., and M. Chidambaram. "Closed-Loop Identification of Multivariable Systems by Optimization Method." Industrial & Engineering Chemistry Research 51, no. 3 (2012): 1324–36. http://dx.doi.org/10.1021/ie2013778.

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38

Chen, Huixin, and Jan Maciejowski. "Subspace Identification Method for Combined Deterministic-Stochastic Bilinear Systems." IFAC Proceedings Volumes 33, no. 15 (2000): 229–34. http://dx.doi.org/10.1016/s1474-6670(17)39755-0.

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39

Carroll, T. L. "Phase space method for identification of driven nonlinear systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 3 (2009): 033121. http://dx.doi.org/10.1063/1.3207836.

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40

Wang, Weimin, Qihang Li, Jinji Gao, Jianfei Yao, and Paul Allaire. "An identification method for damping ratio in rotor systems." Mechanical Systems and Signal Processing 68-69 (February 2016): 536–54. http://dx.doi.org/10.1016/j.ymssp.2015.05.023.

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41

Gorcin, Ali, and Huseyin Arslan. "An OFDM Signal Identification Method for Wireless Communications Systems." IEEE Transactions on Vehicular Technology 64, no. 12 (2015): 5688–700. http://dx.doi.org/10.1109/tvt.2015.2388671.

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42

HWANG, CHYI, and MUH-YANG CHEN. "Parameter identification of bilinear systems using the Galerkin method." International Journal of Systems Science 16, no. 5 (1985): 641–48. http://dx.doi.org/10.1080/00207728508926701.

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43

Sorokina, Mariia, Stylianos Sygletos, and Sergei Turitsyn. "Sparse identification for nonlinear optical communication systems: SINO method." Optics Express 24, no. 26 (2016): 30433. http://dx.doi.org/10.1364/oe.24.030433.

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44

Ichikawa, Satoshi, Masanori Yamamoto, and Yasuhiro Nishigawara. "An identification method of nonlinear systems by volterra series." Electronics and Communications in Japan (Part I: Communications) 71, no. 2 (1988): 39–51. http://dx.doi.org/10.1002/ecja.4410710205.

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45

Zhang, Ying, and Chun-Bo Feng. "Identification of closed-loop systems via least-squares method." International Journal of Adaptive Control and Signal Processing 12, no. 1 (1998): 29–39. http://dx.doi.org/10.1002/(sici)1099-1115(199802)12:1<29::aid-acs465>3.0.co;2-s.

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46

Wani Jamaludin, Irma Wani Jamaludin, and Norhaliza Abdul Wahab. "Recursive Subspace Identification Algorithm using the Propagator Based Method." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (2017): 172. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp172-179.

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<p>Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the

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47

Lassoued, Zeineb, and Kamel Abderrahim. "New Approaches to Identification of PWARX Systems." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/845826.

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We consider the clustering-based procedures for the identification of discrete-time hybrid systems in the piecewise affine (PWA) form. These methods exploit three main techniques which are clustering, linear identification, and pattern recognition. The clustering method based on thek-means algorithm is treated in this paper. It consists in estimating both the parameter vector of each submodel and the coefficients of each partition while knowing the model ordersnaandnband the number of submodelss. The performance of this approach can be threatened by the presence of outliers and poor initializa

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48

Journal, Baghdad Science. "Bilinear System Identification Using Subspace Method." Baghdad Science Journal 4, no. 4 (2007): 670–75. http://dx.doi.org/10.21123/bsj.4.4.670-675.

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In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-block " and " four-block " subspace algorithms are used. In this algorithms the input signal to the system does not have to be white . Simulation of these algorithms shows that the " four-block " gives fast convergence and the dimensions of the matrices involved are significantly smaller so that the computational complexity is lower as a comparison with " three-block " algorithm .

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49

Jasim, Wesam M. "Bilinear System Identification Using Subspace Method." Baghdad Science Journal 4, no. 4 (2021): 670–75. http://dx.doi.org/10.21123/bsj.2007.4.4.670-675.

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In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-block " and " four-block " subspace algorithms are used. In this algorithms the input signal to the system does not have to be white . Simulation of these algorithms shows that the " four-block " gives fast convergence and the dimensions of the matrices involved are significantly smaller so that the computational complexity is lower as a comparison with " three-block " algorithm .

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50

Karabutov, N. N. "On adaptive identification of systems having multiple nonlinearities." Russian Technological Journal 11, no. 5 (2023): 94–105. http://dx.doi.org/10.32362/2500-316x-2023-11-5-94-10.

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Objectives. The solution to the relevant problem of identifying systems with multiple nonlinearities depends on such factors as feedback, ways of connecting nonlinear links, and signal properties. The specifics of nonlinear systems affect control systems design methods. As a rule, the basis for the development of a mathematical model involves the linearization of a system. Under conditions of uncertainty, the identification problem becomes even more relevant. Therefore, the present work sets out to develop an approach to the identification of nonlinear dynamical systems under conditions of unc

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