Статті в журналах: "Linear and Nonlinear System identification"

1

Wang, Shuning, and Masahiro Tanaka. "Nonlinear system identification with piecewise-linear functions." IFAC Proceedings Volumes 32, no. 2 (July 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 done using a unique stage.

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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

Spanos, P. D., and R. Lu. "Nonlinear System Identification in Offshore Structural Reliability." Journal of Offshore Mechanics and Arctic Engineering 117, no. 3 (August 1, 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 procedure is applied to decouple these linear sub-systems; a frequency domain technique involving autospectra and cross-spectra is employed to identify the linear transfer functions. The structural properties and the force transfer parameters are determined with the aid of the coherence functions. The method is verified using simulated data. It provides a versatile and noniterative approach for dealing with nonlinear interaction problems encountered in offshore structural analysis and design.

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5

Benyassi, Mohamed, Adil Brouri, and Smail Slassi. "Nonlinear systems identification with discontinuous nonlinearity." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (March 6, 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 proposed algorithm can be used of large class of nonlinear systems.</span>

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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

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 arbitrary spectral properties. Results in this article prove that serious errors can occur when conventional linear model analysis procedures are used to determine the physical properties of nonlinear systems.

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8

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

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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 parameters. Then, based on this function and with the least square method, physical parameters of the system are identified. Finally, the applicability of the proposed technique is confirmed by numerical simulation.

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10

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 (March 28, 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 input measurement, is presented that uses information about the nonlinear elements of the system to estimate the underlying linear parameters. Restoring force, boundary perturbation, and direct parameter estimation techniques are combined to develop this approach. The approach is applied to experimental tire-vehicle suspension system data.

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11

Prasad, Vineet, Kajal Kothari, and Utkal Mehta. "Parametric Identification of Nonlinear Fractional Hammerstein Models." Fractal and Fractional 4, no. 1 (December 30, 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 is indicated by concurrent identification of the fractional order with linear system coefficients, algebraic representation of the immeasurable nonlinear static function output, and permitting use of non-iterative procedures for identification of the nonlinearity. The efficacy of the proposed method is exhibited through simulation at various signal-to-noise ratios.

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12

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 (February 3, 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 the effectiveness of the used algorithm to identify, model, and write down as simple sub-systems, a complex black box system.

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13

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 (January 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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14

Messner, W., and R. Horowitz. "Identification of a Nonlinear Function in a Dynamical System." Journal of Dynamic Systems, Measurement, and Control 115, no. 4 (December 1, 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 identified. This paper focuses on an application to disk drive servos.

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15

Chawla, Ishan, and Ashish Singla. "ANFIS based system identification of underactuated systems." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 7-8 (November 18, 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 the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.

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16

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 effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

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17

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 technique in the presence of the system nonlinearity. Several frequency- and time-domain based OMA techniques are examined for the modal identification of simulated and real nonlinear mechanical systems. Theoretical analyses are also provided to understand how the system nonlinearity degrades the performance of the OMA algorithms.

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18

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 (December 15, 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 measured. CSP was perturbed using a Gaussian white noise signal. A second-order Volterra model was developed by applying nonparametric identification to the measurements. The second-order kernel was mainly diagonal, but the diagonal differed in shape from the first-order kernel. Hence, a reduced second-order model was similarly developed comprising a linear dynamic system in parallel with a squaring system in cascade with a slower linear dynamic system. This “Uryson” model predicted AP changes 12% better ( P < 0.01) than a linear model in response to new Gaussian white noise CSP. The model also predicted nonlinear behaviors, including thresholding and mean responses to CSP changes about the mean. Models of the neural arc (the system relating CSP to SNA) and peripheral arc (the system relating SNA to AP) were likewise developed and tested. However, these models of subsystems of the total arc showed approximately linear behaviors. In conclusion, the validated nonlinear model of the total arc revealed that the system takes on an Uryson structure.

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19

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-change scheme, in order to extract both the nonlinear state-space model and the underlying linear system. The technique is tested first on a numerical nonlinear system and subsequently on experimental measurements of a multi-degree-of-freedom system comprising a localized nonlinearity.

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20

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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21

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

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22

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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23

Karimov, Artur I., Ekaterina Kopets, Erivelton G. Nepomuceno, and Denis Butusov. "Integrate-and-Differentiate Approach to Nonlinear System Identification." Mathematics 9, no. 23 (November 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 least squares method by including an auxiliary reclassification step into it. The application of this method allows us to improve the identification accuracy through the elimination of classification errors needed for parameter estimation of piece-wise linear differential equations. Simulation of the Duffing-like chaotic mechanical system and experimental study of an aluminum beam with asymmetric joint show that the proposed approach is more accurate than state-of-the-art solutions.

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24

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 and the high numerical performances of the subspace algorithms are successfully exploited, as shown by the implementation of the proposed method on simulated multi-degree-of-freedom systems with typical nonlinear characteristics as well as on an experimental case. These examples demonstrate that the application of subspace algorithms to nonlinear system identification gives better conditioning and computational efficiency with respect to the most recent nonlinear techniques. Moreover, the capability of the NSI method of simultaneously dealing with several nonlinear terms, with a light computational effort, may be also exploited in those situations where no a priori knowledge of the location and the type of nonlinearities is given, being this method well capable of detecting the contribution of the dominant nonlinearities.On the basis of the results discussed in this paper, and compared with those of other well-assessed nonlinear techniques, the proposed method appears having the chances to become a robust procedure to be widely exploited in many industrial fields, being its capability of separating linear and nonlinear contribution terms widely requested in mechanical and civil engineering field.

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25

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

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26

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

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27

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 (May 1, 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 hydrodynamically damped for the RV model, and (B.1) nonlinear-structure nonlinearly damped for the IFF model, are developed for an experimental submerged-sphere nonlinear mooring system under ocean waves. The analytic models and the associated algorithms for parametric identification are described. In this companion paper (Part 2), we use the experimentally measured input wave and output system response data and apply the algorithms derived based on the multiple-input/single-output linear analysis of the reverse dynamic systems to identify the system parameters. The two nonlinear models are examined in detail and the most suitable physical representative model is selected for the mooring system considered. A sensitive analysis is conducted to investigate the coupled hydrodynamic forces modeled by the Morison equation, the nonlinear stiffness from mooring lines and the nonlinear response. The appropriateness of each model is discussed in detail.

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28

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 conventional nonlinear models has been observed and reported, indicating the effectiveness of the proposed model in identifying nonlinear systems.

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29

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 with nonlinear elements.

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30

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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Анотація:

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 problems; detecting and quantifying nonlinearities in structures. In contrary, the ORP is a new algorithm developed in time-domain which gives simpler formulation for describing the underlying linear dynamics of nonlinear systems. Results show that the performance of the new ORP algorithm in handling nonlinearities is as good as the CRP method. The ability of ORP method has become the aim of the current study to assess the robustness of both algorithms towards nonlinear system identification of structures with multi-degree-of-freedom (MDOF) system.

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31

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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Анотація:

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 based on PID neural network. The structure and algorithm of PID neural network are introduced and the properties and characteristics are analyzed. The system identification is completed and the results are fast convergence.

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32

Vörös, Jozef. "Identification of Nonlinear Cascade Systems with Time-Varying Backlash." Journal of Electrical Engineering 62, no. 2 (March 1, 2011): 87–92. http://dx.doi.org/10.2478/v10187-011-0014-2.

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Identification of Nonlinear Cascade Systems with Time-Varying BacklashRecursive identification of cascade systems with time-varying input backlash and linear dynamic system is presented. A new analytic form of backlash characteristic description is used, hence all the parameters in the cascade model equation are separated and their estimation is solved as a quasi-linear problem using the recursive least squares method with internal variable estimation. Simulation studies are included.

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33

Yi, Kyongsu, and Karl Hedrick. "Observer-Based Identification of Nonlinear System Parameters." Journal of Dynamic Systems, Measurement, and Control 117, no. 2 (June 1, 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. The observer is used to estimate signals which are difficult or expensive to measure. Using the estimated states of the system with repetitive excitation, the parameter estimates are obtained. The observer based identification method has been tested on a half car simulation and used to identify the parameters of a half car suspension test rig. The estimates of nonlinear damping coefficients of a vehicle suspension, suspension stiffness, pitch moment inertia, equivalent sprung mass, and unsprung mass are obtained by the proposed method. Simulation and experimental results show that the identifier estimates the vehicle parameters accurately. The proposed identifier will be useful for parameter identification of actual vehicles since vehicle parameters can be identified only using vehicle excitation tests rather than component testing.

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34

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, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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35

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 then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.

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36

Aissaoui, Borhen, Moêz Soltani, and Abdelkader Chaari. "Subspace Identification of Hammerstein Model with Unified Discontinuous Nonlinearity." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1794921.

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The main aim of this study is to handle the case where the structures of nonlinear systems are unknown. In the many works, the parametric identification of nonlinear systems represented by Hammerstein model, with discontinuous and asymmetric nonlinearity, considers the structures of the nonlinear and linear blocks are known, especially the nonlinear bloc. To solve this problem, a unified form of nonlinearity representing eight cases of nonlinearities can be used. The parameters of both blocks, linear and nonlinear, are estimated using an iterative subspace approach. More importantly, in an attempt to show the extent to which this method is efficient, we apply it to experimental data obtained from the electropneumatic system. As a result, the numerical and experimental examples confirm a good conditioning and computational efficiency.

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37

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 (April 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 separation of system effectively. Simulation results show that this method can track the changes of arc characteristics effectively. That it achieves the aim of real-time monitoring and controlling system parameters.</p>

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38

Zhang, Shuo, Dongqing Wang, and Yaru Yan. "Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems." Complexity 2018 (July 22, 2018): 1–10. http://dx.doi.org/10.1155/2018/8420426.

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Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system. The idea is, by the filtering technique and the instrumental variable method, to transform the Hammerstein system into a simple form with a separated nonlinear expression and to parameterize the system into an autoregressive model, then to perform an instrumental variable-based orthogonal matching pursuit (IV-OMP) identification method for the Hammerstein system. Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency.

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39

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 (March 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 experiences are discussed, and a simple numerical example is provided for illustration.

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40

Narayanan, S., and S. C. S. Yim. "Modeling and Identification of a Nonlinear SDOF Moored Structure, Part 1—Hydrodynamic Models and Algorithms." Journal of Offshore Mechanics and Arctic Engineering 126, no. 2 (May 1, 2004): 175–82. http://dx.doi.org/10.1115/1.1710875.

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The highly nonlinear responses of compliant ocean structures characterized by a large-geometry restoring force and coupled fluid-structure interaction excitation are of great interest to ocean and coastal engineers. Practical modeling, parameter identification, and incorporation of the inherent nonlinear dynamics in the design of these systems are essential and challenging. The general approach of a nonlinear system technique using very simple models has been presented in the literature by Bendat. In Part 1 of this two-part study, two specific 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 reverse multiple-input/single-output (R-MI/SO) system-identification technique: (A.1) nonlinear-structure linearly damped, and (A.2) nonlinear-structure coupled hydrodynamically damped for the RV model, and (B.1) nonlinear-structure nonlinearly damped for the IFF model, are developed for a specific experimental submerged-sphere mooring system under ocean waves exhibiting such highly nonlinear response behaviors. In Part 2, using the measured input wave and output system response data, the algorithms derived based on the MI/SO linear analysis of the reverse dynamic systems are applied to identify the properties of the highly nonlinear system. Practical issues on the application of the R-MI/SO technique based on limited available experimental data are addressed.

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41

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 of nonlinear block parameters the identification method is built using one stage.

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42

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 (March 28, 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 approaches such as the unscented Kalman filter (UKF) have been proposed for the identification of strong nonlinear problems. Based on the fact that nonlinearities exist in local areas of structures, a straightforward two-stage identification approach is proposed in this paper for the identification of strong nonlinear structural parameters with incomplete response measurements. In the first stage, the locations of nonlinearities are identified based on the EKF for the identification of the equivalent linear structures. In the second stage, the UKF is utilized to identify the parameters of strong nonlinear structural systems. Therefore, the parametric identification of strong nonlinear structural parameters is simplified by the proposed approach. Several numerical examples with different nonlinear models and locations are used to validate the proposed approach.

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43

Dogariu, Laura-Maria, Silviu Ciochină, Jacob Benesty, and Constantin Paleologu. "System Identification Based on Tensor Decompositions: A Trilinear Approach." Symmetry 11, no. 4 (April 17, 2019): 556. http://dx.doi.org/10.3390/sym11040556.

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The theory of nonlinear systems can currently be encountered in many important fields, while the nonlinear behavior of electronic systems and devices has been studied for a long time. However, a global approach for dealing with nonlinear systems does not exist and the methods to address this problem differ depending on the application and on the types of nonlinearities. An interesting category of nonlinear systems is one that can be regarded as an ensemble of (approximately) linear systems. Some popular examples in this context are nonlinear electronic devices (such as acoustic echo cancellers, which are used in applications for two-party or multi-party voice communications, e.g., videoconferencing), which can be modeled as a cascade of linear and nonlinear systems, similar to the Hammerstein model. Multiple-input/single-output (MISO) systems can also be regarded as separable multilinear systems and be treated using the appropriate methods. The high dimension of the parameter space in such problems can be addressed with methods based on tensor decompositions and modelling. In recent work, we focused on a particular type of multilinear structure—namely the bilinear form (i.e., two-dimensional decompositions)—in the framework of identifying spatiotemporal models. In this paper, we extend the work to the decomposition of more complex systems and we propose an iterative Wiener filter tailored for the identification of trilinear forms (where third-order tensors are involved), which can then be further extended to higher order multilinear structures. In addition, we derive the least-mean-square (LMS) and normalized LMS (NLMS) algorithms tailored for such trilinear forms. Simulations performed in the context of system identification (based on the MISO system approach) indicate the good performance of the proposed solution, as compared to conventional approaches.

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44

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 (December 1997): 3817. http://dx.doi.org/10.1121/1.420285.

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45

Zhu, C., and F. W. Paul. "A Fourier Series Neural Network and Its Application to System Identification." Journal of Dynamic Systems, Measurement, and Control 117, no. 3 (September 1, 1995): 253–61. http://dx.doi.org/10.1115/1.2799114.

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A distinctive neural network architecture, called the Fourier Series Neural Network (FSNN), is developed with particular consideration for applications in the area of system identification and control. This paper focuses on the theory of the FSNN and its application to system identification. This neural network is based on the topological structure of the multiple Fourier series, and is shown to be free of local minima. The global stability of the FSNN learning dynamics is guaranteed using the Delta learning rule. This paper demonstrates that the trained FSNN model approximates the Fourier series representation of an identified system with the network state weights approximating the coefficients of the Fourier series. This feature enables the FSNN to estimate the frequency spectrum of an unknown system, making the FSNN a powerful tool for controller design or on-line adaptive tuning based on system frequency response. The capabilities of the FSNN are demonstrated for linear and nonlinear systems by applying the FSNN to estimate the amplitude and phase spectrums of a second order linear transfer function and to model nonlinear inverse robot kinematics. These evaluations indicate that the FSNN modeling technique is applicable to both linear and nonlinear systems with multi-inputs and multi-outputs.

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46

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 identification. Then the extended Kalman filter is applied to simultaneously identify state vector including the structural parameters and excitation orthogonal parameters in state space based on incomplete measurements. The proposed method is validated numerically with the simulation of three-story linear and nonlinear structures subject to external force. The external force on the top floor and the structural parameters are simultaneously identified with the proposed system identification method. Results from both simulations indicate that the proposed method is capable of identifing the dynamic load and structural parameters fairly accurately with contaminated incomplete measurement for both of the linear and nonlinear structural systems.

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47

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 using the significance test, allowing the proposed method to detect and locate the most relevant nonlinearities of the practical structure. Experimental results demonstrate that the proposed method is able to decouple the linear and nonlinear dynamics of a nonlinear structure and estimate natural frequencies of the derived linear system along with nonlinear internal forces in one computational step, even if no a priori knowledge of the type of nonlinearities is given.

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48

Brouri, A., and S. Slassi. "Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 167. http://dx.doi.org/10.11591/ijece.v6i1.8694.

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Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs.

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49

Brouri, A., and S. Slassi. "Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 167. http://dx.doi.org/10.11591/ijece.v6i1.pp167-176.

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Анотація:

Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs.

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50

MUROI, Hideo, Takashi SHIKIMORI, and Shuichi ADACHI. "A Nonlinear System Identification Method Based on Local Linear PLS Method." Transactions of the Society of Instrument and Control Engineers 49, no. 3 (2013): 378–85. http://dx.doi.org/10.9746/sicetr.49.378.

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