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Journal articles on the topic 'Linear and Nonlinear System identification'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Wang, Shuning, and Masahiro Tanaka. "Nonlinear system identification with piecewise-linear functions." IFAC Proceedings Volumes 32, no. 2 (1999): 3796–801. http://dx.doi.org/10.1016/s1474-6670(17)56648-3.

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2

Benyassi, Mohamed, and Adil Brouri. "Identification of Nonparametric Nonlinear Systems." ITM Web of Conferences 24 (2019): 02006. http://dx.doi.org/10.1051/itmconf/20192402006.

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Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be

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3

Nakamura, Akira, and Nozomu Hamada. "Identification of nonlinear dynamical system by piecewise-linear system." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 74, no. 9 (1991): 102–15. http://dx.doi.org/10.1002/ecjc.4430740911.

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4

Benyassi, Mohamed, and Adil Brouri. "Estimation of nonlinear systems parameters." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2020): 26–33. https://doi.org/10.11591/ijra.v9i1.pp26-33.

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In this paper, an identification method is proposed to determine the nonlinear systems parameters. The proposed nonlinear systems can be described by Wiener systems. This structure of models consists of series of linear dynamic element and a nonlinearity block. Both the linear and nonlinear parts are nonparametric. In particular, the linear subsystem of structure entirely unknown. The considered nonlinearity function is of hard type. This latter can have a dead zone or with preload. These nonlinear systems have been confirmed by several practical applications

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5

Li, Yingying, Tianpeng Zhang, Subhro Das, Jeff Shamma, and Na Li. "Non-asymptotic System Identification for Linear Systems with Nonlinear Policies." IFAC-PapersOnLine 56, no. 2 (2023): 1672–79. http://dx.doi.org/10.1016/j.ifacol.2023.10.1872.

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6

Potts, Duncan, and Claude Sammut. "ONLINE NONLINEAR SYSTEM IDENTIFICATION USING LINEAR MODEL TREES." IFAC Proceedings Volumes 38, no. 1 (2005): 202–7. http://dx.doi.org/10.3182/20050703-6-cz-1902.00034.

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7

Spanos, P. D., and R. Lu. "Nonlinear System Identification in Offshore Structural Reliability." Journal of Offshore Mechanics and Arctic Engineering 117, no. 3 (1995): 171–77. http://dx.doi.org/10.1115/1.2827086.

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Nonlinear forces acting on offshore structures are examined from a system identification perspective. The nonlinearities are induced by ocean waves and may become significant in many situations. They are not necessarily in the form of Morison’s equation. Various wave force models are examined. The force function is either decomposed into a set of base functions or it is expanded in terms of the wave and structural kinematics. The resulting nonlinear system is decomposed into a number of parallel no-memory nonlinear systems, each followed by a finite-memory linear system. A conditioning procedu

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8

Bendat, Julius S. "Spectral Techniques for Nonlinear System Analysis and Identification." Shock and Vibration 1, no. 1 (1993): 21–31. http://dx.doi.org/10.1155/1993/438416.

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This article reviews some recent and current research work with emphasis on new recommended spectral techniques that can analyze and identify the optimum linear and nonlinear system properties in a large class of single-input/single-output nonlinear models by using experimentally measured input/output random data. This is done by showing how to replace these nonlinear models with equivalent multiple-input/single-output linear models that are solvable by well-established practical procedures. The input random data can have probability density functions that are Gaussian or non-Gaussian with arb

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9

Peng, Jiehua, Jiashi Tang, and Zili Chen. "Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain." Shock and Vibration 11, no. 5-6 (2004): 685–92. http://dx.doi.org/10.1155/2004/634785.

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A new method of identifying parameters of nonlinearly vibrating system in frequency domain is presented in this paper. The problems of parameter identification of the nonlinear dynamic system with nonlinear elastic force or nonlinear damping force are discussed. In the method, the mathematic model of parameter identification is frequency response function. Firstly, by means of perturbation method the frequency response function of weakly nonlinear vibration system is derived. Next, a parameter transformation is made and the frequency response function becomes a linear function of the new param

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10

Benyassi, Mohamed, Adil Brouri, and Smail Slassi. "Nonlinear systems identification with discontinuous nonlinearity." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2019): 34. http://dx.doi.org/10.11591/ijra.v9i1.pp34-41.

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<span>In this paper, nonparametric nonlinear systems identification is proposed. The considered system nonlinearity is nonparametric and is of hard type. This latter can be discontinuous and noninvertible. The entire nonlinear system is structured by Hammerstein model. Furthermore, the linear dynamic block is of any order and can be nonparametric. The problem identification method is done within two stages. In the first stage, the system nonlinearity is identified using simple input signals. In the first stage, the linear dynamic block parameters are estimated using periodic signals. The

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11

Benyassi, Mohamed, Adil Brouri, and Smail Slassi. "Nonlinear systems identification with discontinuous nonlinearity." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2020): 34–41. https://doi.org/10.11591/ijra.v9i1.pp34-41.

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In this paper, nonparametric nonlinear systems identification is proposed. The considered system nonlinearity is nonparametric and is of hard type. This latter can be discontinuous and noninvertible. The entire nonlinear system is structured by Hammerstein model. Furthermore, the linear dynamic block is of any order and can be nonparametric. The problem identification method is done within two stages. In the first stage, the system nonlinearity is identified using simple input signals. In the first stage, the linear dynamic block parameters are estimated using per

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12

Prasad, Vineet, Kajal Kothari, and Utkal Mehta. "Parametric Identification of Nonlinear Fractional Hammerstein Models." Fractal and Fractional 4, no. 1 (2019): 2. http://dx.doi.org/10.3390/fractalfract4010002.

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In this paper, a system identification method for continuous fractional-order Hammerstein models is proposed. A block structured nonlinear system constituting a static nonlinear block followed by a fractional-order linear dynamic system is considered. The fractional differential operator is represented through the generalized operational matrix of block pulse functions to reduce computational complexity. A special test signal is developed to isolate the identification of the nonlinear static function from that of the fractional-order linear dynamic system. The merit of the proposed technique i

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13

Khouni, S., and K. E. Hemsas. "Nonlinear System Identification using Uncoupled State Multi-model Approach: Application to the PCB Soldering System." Engineering, Technology & Applied Science Research 10, no. 1 (2020): 5221–27. https://doi.org/10.5281/zenodo.3659560.

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Multi-model approach is an adapted tool of modeling nonlinear systems. The underlying idea is to simplify the complex nature of the system to be studied by decomposing it into simple (linear) sub-systems, in order to simplify the study (stability, control law, surveillance, etc.). This technique allows us to extend the application of linear systems methodology to nonlinear systems. This paper presents nonlinear system identification using an uncoupled state multi-model applied to a Printed Circuit Boards (PCB) soldering system. Precision, simplicity, and fidelity of the obtained results show t

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14

Hayajnh, Mahmoud A., Umberto Saetti, and J. V. R. Prasad. "Identification of High-Order Linear Time-Invariant Models from Periodic Nonlinear System Responses." Aerospace 11, no. 11 (2024): 875. http://dx.doi.org/10.3390/aerospace11110875.

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This paper presents a novel step in the extension of subspace identification toward the direct identification of harmonic decomposition linear time-invariant models from nonlinear time-periodic system responses. The proposed methodology is demonstrated through examples involving the nonlinear time-periodic dynamics of a flapping-wing micro aerial vehicle. These examples focus on the identification of the vertical dynamics from various types of input–output data, including linear time-invariant, linear time-periodic, and nonlinear time-periodic input–output data. A harmonic analyzer is used to

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15

Huang, Xiaolin, Jun Xu, and Shuning Wang. "Nonlinear system identification with continuous piecewise linear neural network." Neurocomputing 77, no. 1 (2012): 167–77. http://dx.doi.org/10.1016/j.neucom.2011.09.001.

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16

Messner, W., and R. Horowitz. "Identification of a Nonlinear Function in a Dynamical System." Journal of Dynamic Systems, Measurement, and Control 115, no. 4 (1993): 587–91. http://dx.doi.org/10.1115/1.2899184.

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Using an adaptive method introduced in (Messner et al., 1991), a standard identification technique for linear systems can be extended to identify nonlinear functions in dynamical systems under a mild condition. Specifically, the assumption is that the nonlinear function can be represented as a integral equation of the first kind. The method identifies the nonlinear function indirectly by estimating the influence function of the integral equation. By analogy to linear methods the kernel of the integral equation serves as the “regressor,” while the influence function is the “parameter” to be ide

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17

Chawla, Ishan, and Ashish Singla. "ANFIS based system identification of underactuated systems." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 7-8 (2020): 649–60. http://dx.doi.org/10.1515/ijnsns-2018-0005.

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AbstractIn this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of t

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18

Haroon, Muhammad, Douglas E. Adams, and Yiu Wah Luk. "A Technique for Estimating Linear Parameters Using Nonlinear Restoring Force Extraction in the Absence of an Input Measurement." Journal of Vibration and Acoustics 127, no. 5 (2005): 483–92. http://dx.doi.org/10.1115/1.2013293.

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Conventional nonlinear system identification procedures estimate the system parameters in two stages. First, the nominally linear system parameters are estimated by exciting the system at an amplitude (usually low) where the behavior is nominally linear. Second, the nominally linear parameters are used to estimate the nonlinear parameters of the system at other arbitrary amplitudes. This approach is not suitable for many mechanical systems, which are not nominally linear over a broad frequency range for any operating amplitude. A method for nonlinear system identification, in the absence of an

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19

Coca, D., and S. A. Billings. "Continuous-Time System Identification for Linear and Nonlinear Systems Using Wavelet Decompositions." International Journal of Bifurcation and Chaos 07, no. 01 (1997): 87–96. http://dx.doi.org/10.1142/s0218127497000066.

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A new approach for estimating linear and nonlinear continuous-time models directly from noisy observations is introduced using wavelet decompositions. Results using both simulated and experimental data are included to demonstrate the performance of the new algorithm.

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20

Moslehpour, Mohsen, Toru Kawada, Kenji Sunagawa, Masaru Sugimachi, and Ramakrishna Mukkamala. "Nonlinear identification of the total baroreflex arc." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 309, no. 12 (2015): R1479—R1489. http://dx.doi.org/10.1152/ajpregu.00278.2015.

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The total baroreflex arc [the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP)] is known to exhibit nonlinear behaviors. However, few studies have quantitatively characterized its nonlinear dynamics. The aim of this study was to develop a nonlinear model of the sympathetically mediated total arc without assuming any model form. Normal rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned, the carotid sinus regions were isolated and attached to a servo-controlled piston pump, and the AP and sympathetic nerve activity (SNA) were

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21

Kara, Tolgay, and Sawsan Abokoos. "A Simulation and Experimental Study on Identification of an Electromechanical System." International Journal of Systems Applications, Engineering & Development 16 (January 9, 2022): 26–33. http://dx.doi.org/10.46300/91015.2022.16.5.

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The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effectiv

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22

Wang, Lan, Yu Cheng, Jinglu Hu, Jinling Liang, and Abdullah M. Dobaie. "Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/8197602.

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Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model,

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23

Zabiri, Haslinda, Marappagounder Ramasamy, Tufa Dendena Lemma, and Abdul Maulud. "Identification of Nonlinear Systems Using Parallel Laguerre-NN Model." Advanced Materials Research 785-786 (September 2013): 1430–36. http://dx.doi.org/10.4028/www.scientific.net/amr.785-786.1430.

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In this paper, a nonlinear system identification framework using parallel linear-plus-neural networks model is developed. The framework is established by combining a linear Laguerre filter model and a nonlinear neural networks (NN) model in a parallel structure. The main advantage of the proposed parallel model is that by having a linear model as the backbone of the overall structure, reasonable models will always be obtained. In addition, such structure provides great potential for further study on extrapolation benefits and control. Similar performance of proposed method with other conventio

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24

Wang, Jinfeng, Shoulin Yin, and Xueying Wang. "A New Electrode Regulator System Identification of Arc Furnace Based on Time-Variant Nonlinear-Linear-Nonlinear Model." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 1 (2016): 32. http://dx.doi.org/10.11591/ijeecs.v2.i1.pp32-39.

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<p>In this paper, we express arc furnace electrode regulator system as a time-variant nonlinear-linear-nonlinear model. On this basis, we propose an online identification method based on nonlinear-linear-nonlinear model system. This new scheme solves the problem of model variation and prediction precision decline causing by time-varying of arc characteristic. In order to dispose the difficulty of parameters separation in the online identification process, this new method adopts the mind of update the parameters of linear parts and nonlinear parts respectively. It realizes the parameters

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25

Karimov, Artur I., Ekaterina Kopets, Erivelton G. Nepomuceno, and Denis Butusov. "Integrate-and-Differentiate Approach to Nonlinear System Identification." Mathematics 9, no. 23 (2021): 2999. http://dx.doi.org/10.3390/math9232999.

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In this paper, we consider a problem of parametric identification of a piece-wise linear mechanical system described by ordinary differential equations. We reconstruct the phase space of the investigated system from accelerometer data and perform parameter identification using iteratively reweighted least squares. Two key features of our study are as follows. First, we use a differentiated governing equation containing acceleration and velocity as the main independent variables instead of the conventional governing equation in velocity and position. Second, we modify the iteratively reweighted

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26

Yim, S. C. S., and S. Narayanan. "Modeling and Identification of a Nonlinear SDOF Moored Structure, Part 2—Comparisons and Sensitivity Study." Journal of Offshore Mechanics and Arctic Engineering 126, no. 2 (2004): 183–90. http://dx.doi.org/10.1115/1.1710874.

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A system-identification technique based on the Reverse Multiple-Input/Single-Output (R-MI/SO) procedure is applied to identify the parameters of an experimental mooring system exhibiting nonlinear behavior. In Part 1, two nonlinear small-body hydrodynamic Morison type formulations: (A) with a relative-velocity (RV) model, and (B) with an independent-flow-field (IFF) model, are formulated. Their associated nonlinear system-identification algorithms based on the R-MI/SO system-identification technique: (A.1) nonlinear-structure linearly damped, and (A.2) nonlinear-structure coupled hydrodynamica

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27

Yan, Jun, Bo Li, Hai-Feng Ling, Hai-Song Chen, and Mei-Jun Zhang. "Nonlinear State Space Modeling and System Identification for Electrohydraulic Control." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/973903.

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The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W) model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and

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28

Khouni, S., and K. E. Hemsas. "Nonlinear System Identification using Uncoupled State Multi-model Approach: Application to the PCB Soldering System." Engineering, Technology & Applied Science Research 10, no. 1 (2020): 5221–27. http://dx.doi.org/10.48084/etasr.3247.

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Multi-model approach is an adapted tool of modeling nonlinear systems. The underlying idea is to simplify the complex nature of the system to be studied by decomposing it into simple (linear) sub-systems, in order to simplify the study (stability, control law, surveillance, etc.). This technique allows us to extend the application of linear systems methodology to nonlinear systems. This paper presents nonlinear system identification using an uncoupled state multi-model applied to a Printed Circuit Boards (PCB) soldering system. Precision, simplicity, and fidelity of the obtained results show t

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29

Schoukens, J., R. Pintelon, T. Dobrowiecki, and Y. Rolain. "Identification of Linear Systems with Nonlinear Distortions." IFAC Proceedings Volumes 36, no. 16 (2003): 1723–34. http://dx.doi.org/10.1016/s1474-6670(17)35009-7.

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30

Schoukens, J., R. Pintelon, T. Dobrowiecki, and Y. Rolain. "Identification of linear systems with nonlinear distortions." Automatica 41, no. 3 (2005): 491–504. http://dx.doi.org/10.1016/j.automatica.2004.10.004.

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31

Schoukens, J., K. Barbé, L. Vanbeylen, and R. Pintelon. "ANALYSIS OF THE NONLINEAR INDUCED VARIANCE IN LINEAR SYSTEM IDENTIFICATION." IFAC Proceedings Volumes 42, no. 10 (2009): 634–39. http://dx.doi.org/10.3182/20090706-3-fr-2004.00105.

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Nelles, Oliver, Alexander Fink, and Rolf Isermann. "Local Linear Model Trees (LOLIMOT) Toolbox for Nonlinear System Identification." IFAC Proceedings Volumes 33, no. 15 (2000): 845–50. http://dx.doi.org/10.1016/s1474-6670(17)39858-0.

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33

Chon, Ki H., R. Mukkamala, K. Toska, T. J. Mullen, A. A. Armoundas, and R. J. Cohen. "Linear and nonlinear system identification of autonomic heart-rate modulation." IEEE Engineering in Medicine and Biology Magazine 16, no. 5 (1997): 96–105. http://dx.doi.org/10.1109/51.620500.

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34

Anastasio, Dario, and Stefano Marchesiello. "Free-Decay Nonlinear System Identification via Mass-Change Scheme." Shock and Vibration 2019 (July 7, 2019): 1–14. http://dx.doi.org/10.1155/2019/1759198.

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Methods for nonlinear system identification of structures generally require input-output measured data to estimate the nonlinear model, as a consequence of the noninvariance of the FRFs in nonlinear systems. However, providing a continuous forcing input to the structure may be difficult or impracticable in some situations, while it may be much easier to only measure the output. This paper deals with the identification of nonlinear mechanical vibrations using output-only free-decay data. The presented method is based on the nonlinear subspace identification (NSI) technique combined with a mass-

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35

Liang, Yanchun, Qiang Zhen, and Zaishen Wang. "Numerical Study on Identification of Time Varying Parameters of Vibration Systems." Shock and Vibration 4, no. 1 (1997): 69–76. http://dx.doi.org/10.1155/1997/495860.

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An on-line least squares algorithm has previously been successfully applied to linear vibration systems in order to identify time varying parameters. In this article the limitations of the approach and the factors affecting the identification are further examined. The existence of the nonlinear term is determined by means of the time varying characteristics of the estimated linear parameters using the linear model and the data from a time invariant nonlinear system. The identification of the time varying linear parameters is also examined in accordance with the linear model by using the data w

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Liang, Yanchun, Qiang Zhen, and Zaishen Wang. "Numerical Study on Identification of Time Varying Parameters of Vibration Systems." Shock and Vibration 4, no. 1 (1997): 69–76. http://dx.doi.org/10.3233/sav-1997-4106.

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An on‐line least squares algorithm has previously been successfully applied to linear vibration systems in order to identify time varying parameters. In this article the limitations of the approach and the factors affecting the identification are further examined. The existence of the nonlinear term is determined by means of the time varying characteristics of the estimated linear parameters using the linear model and the data from a time invariant nonlinear system. The identification of the time varying linear parameters is also examined in accordance with the linear model by using the data w

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Zhang, E., R. Pintelon, and P. Guillaume. "Modal Identification Using OMA Techniques: Nonlinearity Effect." Shock and Vibration 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/178696.

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This paper is focused on an assessment of the state of the art of operational modal analysis (OMA) methodologies in estimating modal parameters from output responses of nonlinear structures. By means of the Volterra series, the nonlinear structure excited by random excitation is modeled as best linear approximation plus a term representing nonlinear distortions. As the nonlinear distortions are of stochastic nature and thus indistinguishable from the measurement noise, a protocol based on the use of the random phase multisine is proposed to reveal the accuracy and robustness of the linear OMA

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Ma, Zhi-Sai, Bo Wang, Xin Zhang, and Qian Ding. "Nonlinear System Identification of Folding Fins with Freeplay Using Direct Parameter Estimation." International Journal of Aerospace Engineering 2019 (November 15, 2019): 1–8. http://dx.doi.org/10.1155/2019/3978260.

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Folding fins are widely adopted in missiles for the efficient use of space during storage and transportation, while nonlinear behavior of freeplay is inevitable due to the factors such as mismachining tolerance, assembly error, and abrasion. The problem of nonlinear system identification of folding fins with freeplay is considered in this paper. A direct parameter estimation method which can identify the nonlinear system with freeplay under base excitation is proposed and subsequently applied to establish the nonlinear dynamic model of a folding fin. The best set of coefficients is selected by

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Budura, Georgeta, and C. Botoca. "Efficient implementation of the third order RLS adaptive Volterra filter." Facta universitatis - series: Electronics and Energetics 19, no. 1 (2006): 133–41. http://dx.doi.org/10.2298/fuee0601133b.

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Nonlinear adaptive filtering techniques are widely used for the nonlinearities identification in many applications. This paper proposes a new implementation of the third order RLS Volterra filter based on the decomposition of the input vector. Its performances are evaluated in a typical nonlinear system identification application. Different degrees of nonlinearity for the nonlinear system are considered. Comparations, based on the adaptive filter error, are made in all cases with a linear identifier. The experimental results show that the proposed nonlinear identifier has better performances t

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Brouri, Adil, and Mohamed Benyassi. "Spectral Determination of Nonlinear System Parameters." ITM Web of Conferences 24 (2019): 02005. http://dx.doi.org/10.1051/itmconf/20192402005.

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In this paper we propose an identification method of nonlinear system. This later can be structured by Wiener models. The determination of nonlinear system parameters can be done using spectral analysis. The system nonlinearity is allowed to be noninvertible general shape nonlinearity but it must be approximated by a polynomial function. The polynomial degree n can vary from one interval to another. The linear dynamic element is not-necessarily parametric but BIBO stable. In this work, a spectral method is developed allowing the estimates of the complex frequency gain as well as the estimates

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Ding, Y., B. Y. Zhao, and B. Wu. "Structural System Identification with Extended Kalman Filter and Orthogonal Decomposition of Excitation." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/987694.

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Both the structural parameter and external excitation have coupling influence on structural response. A new system identification method in time domain is proposed to simultaneously evaluate structural parameter and external excitation. The method can be used for linear and hysteresis nonlinear structural condition assessment based on incomplete structural responses. In this method, the structural excitation is decomposed by orthogonal approximation. With this approximation, the strongly time-variant excitation identification is transformed to gentle time-variant, even constant parameters iden

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Lee, Lawton H., and Kameshwar Poolla. "Identification of Linear Parameter-Varying Systems Using Nonlinear Programming." Journal of Dynamic Systems, Measurement, and Control 121, no. 1 (1999): 71–78. http://dx.doi.org/10.1115/1.2802444.

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This paper deals with the identification of a linear parameter-varying (LPV) system whose parameter dependence can be written as a linear-fractional transformation (LFT). We formulate an output-error identification problem and present a parameter estimation scheme in which a prediction error-based cost function is minimized using nonlinear programming; its gradients and (approximate) Hessians can be computed using LPV filters and inner products, and identifiable model sets (i.e., local canonical forms) are obtained efficiently using a natural geometrical approach. Some computational issues and

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Zabiri, Haslinda, M. Ariff, Lemma Dendena Tufa, and Marappagounder Ramasamy. "A Comparison Study between Integrated OBFARX-NN and OBF-NN for Modeling of Nonlinear Systems in Extended Regions of Operation." Applied Mechanics and Materials 625 (September 2014): 382–85. http://dx.doi.org/10.4028/www.scientific.net/amm.625.382.

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In this paper the combination of linear and nonlinear models in parallel for nonlinear system identification is investigated. A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters-Auto regressive with exogenous input (OBFARX) and a nonlinear neural network (NN) models is developed. The model performance is then compared against previously developed parallel OBF-NN model in a nonlinear CSTR case study in extended regions of operation (i.e. extrapolation capability).

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Marchesiello, S., and L. Garibaldi. "Subspace-Based Identification of Nonlinear Structures." Shock and Vibration 15, no. 3-4 (2008): 345–54. http://dx.doi.org/10.1155/2008/873183.

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Conventional linear estimators give results contaminated in presence of nonlinearities and the extraction of underlying linear system properties is thus difficult. To overcome this problem, the implementation of a recently developed method, called Nonlinear Subspace Identification (NSI), is considered in this paper, by using the perspective of nonlinearities as unmeasured internal feedback forces. Although its formulation is very simple, particular care has to be taken to reduce the ill-conditioning of the problem, in order to find numerically stable solutions. To this purpose, the robustness

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Shu, Hua, and Huai Lin Shu. "Identification of Multivariable Nonlinear Dynamic System Based on PID Neural Network." Applied Mechanics and Materials 719-720 (January 2015): 475–81. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.475.

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System identification is the basis for control system design. For linear time-invariant systems have a variety of identification methods, identification methods for nonlinear dynamic system is still in the exploratory stage. Nonlinear identification method based on neural network is a simple and effective general method that does not require too much priori experience about the system to be identified. Through training and learning, the network weights are corrected to achieve the purpose of system identification. The paper is about the identification of multivariable nonlinear dynamic system

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Liu, Lijun, Ying Lei, and Mingyu He. "A Two-stage Parametric Identification of Strong Nonlinear Structural Systems with Incomplete Response Measurements." International Journal of Structural Stability and Dynamics 16, no. 04 (2016): 1640022. http://dx.doi.org/10.1142/s0219455416400228.

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Compared with the identification of linear structural parameters, it is more difficult to conduct parametric identification of strong nonlinear structural systems, especially when only incomplete structural responses are available. Although the extended Kalman filter (EKF) is useful for structural identification with partial measurements of structural responses and can be extended for the identification of nonlinear structures, EKF approximates nonlinear system through Taylor series expansion and is therefore not effective for the identification of strong nonlinear structural systems. Other ap

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Chen, Yi-Chung, and Jeen-Shing Wang. "A Hammerstein-Wiener Recurrent Neural Network with Frequency-Domain Eigensystem Realization Algorithm for Unknown System Identification." JUCS - Journal of Universal Computer Science 15, no. (13) (2009): 2547–65. https://doi.org/10.3217/jucs-015-13-2547.

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This paper presents a Hammerstein-Wiener recurrent neural network (HWRNN) with a systematic identification algorithm for identifying unknown dynamic nonlinear systems. The proposed HWRNN resembles the conventional Hammerstein-Wiener model that consists of a linear dynamic subsystem that is sandwiched in between two nonlinear static subsystems. The static nonlinear parts are constituted by feedforward neural networks with nonlinear functions and the dynamic linear part is approximated by a recurrent network with linear activation functions. The novelties of our network include: 1) the structure

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Norfazrina, H. M. Y., P. Muhamad, B. A. Aminudin, M. R. Raihan, A. W. Azella, and R. M. S. Zetty. "Conditioned and Orthogonalised Reverse Path Nonlinear Methods on Multi-Degree-of-Freedom System." Applied Mechanics and Materials 752-753 (April 2015): 558–63. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.558.

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Practical engineering structures commonly display nonlinear dynamic response when damage is present in the system. Hence, the studies on nonlinear system identification have increased within these past few years. Current study is aimed on the structural identification of nonlinear systems based on the extraction of underlying linear frequency response function (FRF). The methods chosen to obtain the FRF are the Conditioned Reverse Path (CRP) and the Orthogonalised Reverse Path (ORP) method. The well-known frequency-domain CRP method has been recognised for its ability in solving nonlinear prob

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Yi, Kyongsu, and Karl Hedrick. "Observer-Based Identification of Nonlinear System Parameters." Journal of Dynamic Systems, Measurement, and Control 117, no. 2 (1995): 175–82. http://dx.doi.org/10.1115/1.2835177.

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This paper deals with an observer-based nonlinear system parameter identification method utilizing repetitive excitation. Although methods for physical parameter identification of both linear and nonlinear systems are already available, they are not attractive from a practical point of view since the methods assume that all the system, x, and the system input are available. The proposed method is based on a “sliding observer” and a least-square method. A sufficient condition for the convergence of the parameter estimates is provided in the case of “Lipschitz” nonlinear second-order systems. Th

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Krishnan, Geetha. "Linear and nonlinear properties of cochlear transduction—Application of a nonlinear system identification technique." Journal of the Acoustical Society of America 102, no. 6 (1997): 3817. http://dx.doi.org/10.1121/1.420285.

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