Relevant bibliographies by topics / Linear and Nonlinear System identification

Academic literature on the topic 'Linear and Nonlinear System identification'

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Published: 14 March 2023

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Linear and Nonlinear System identification"

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Gransten, Johan. "Linear and Nonlinear Identification of Solid Fuel Furnace." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5182.

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The aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies.

A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for the control problems. The system fluctuates periodically and the CO outlets sometimes rise high above the permitted level.

There is little work done in the area of advanced boiler control, but some interesting approaches are described in scientific articles. MPC (Model Predictive Control), nonlinear system identification using ANN (Artificial Neural Network), fuzzy logic, Hµ loop shaping and MIMO (Multiple Input Multiple Output) PID tuning methods have been tested with good results.

Both linear and nonlinear system identification is performed in the thesis. The linear models are able to explain about forty percent of the system behavior and the nonlinear models explain about sixty to eighty percent. The main result is that nonlinear models improve the performance and that there are considerable disturbances complicating the identification. Another identification issue was the feedback during the data collection.

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Enqvist, Martin. "Linear Models of Nonlinear Systems." Doctoral thesis, Linköping : Linköpings universitet, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5330.

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Solomou, Michael. "System identification in the presence of nonlinear distortions using multisine signals." Thesis, University of South Wales, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289160.

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Souza, Júnior Amauri Holanda de. "Regional models and minimal learning machines for nonlinear dynamical system identification." reponame:Repositório Institucional da UFC, 2014. http://www.repositorio.ufc.br/handle/riufc/12481.

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SOUZA JUNIOR, A. H. Regional models and minimal learning machines for nonlinear dynamical system identification. 2014. 116 f. Tese (Doutorado em Engenharia de Teleinformática) – Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2014.
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This thesis addresses the problem of identifying nonlinear dynamic systems from a machine learning perspective. In this context, very little is assumed to be known about the system under investigation, and the only source of information comes from input/output measurements on the system. It corresponds to the black-box modeling approach. Numerous strategies and models have been proposed over the last decades in the machine learning field and applied to modeling tasks in a straightforward way. Despite of this variety, the methods can be roughly categorized into global and local modeling approaches. Global modeling consists in fitting a single regression model to the available data, using the whole set of input and output observations. On the other side of the spectrum stands the local modeling approach, in which the input space is segmented into several small partitions and a specialized regression model is fit to each partition. The first contribution of the thesis is a novel supervised global learning model, the Minimal Learning Machine (MLM). Learning in MLM consists in building a linear mapping between input and output distance matrices and then estimating the nonlinear response from the geometrical configuration of the output points. Given its general formulation, the Minimal Learning Machine is inherently capable of operating on nonlinear regression problems as well as on multidimensional response spaces. Naturally, its characteristics make the MLM able to tackle the system modeling problem. The second significant contribution of the thesis represents a different modeling paradigm, called Regional Modeling (RM), and it is motivated by the parsimonious principle. Regional models stand between the global and local modeling approaches. The proposal consists of a two-level clustering approach in which we first partition the input space using the Self-Organizing Map (SOM), and then perform clustering over the prototypes of the trained SOM. After that, regression models are built over the clusters of SOM prototypes, or regions in the input space. Even though the proposals of the thesis can be thought as quite general regression or supervised learning models, the performance assessment is carried out in the context of system identification. Comprehensive performance evaluation of the proposed models on synthetic and real-world datasets is carried out and the results compared to those achieved by standard global and local models. The experiments illustrate that the proposed methods achieve accuracies that are comparable to, and even better than, more traditional machine learning methods thus offering a valid alternative to such approaches
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Xi, Zhiyu Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Identification and control of nonlinear laboratory processes." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2007. http://handle.unsw.edu.au/1959.4/40461.

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In this thesis, a class of control and identification methods on a typical laboratory process - a ball and beam system - are discussed. The ball and beam is a common laboratory process which contains nonlinearity, a double integrator and time-delay. In our project, the hardware made by Wincon (Quanser SRV02 +BB01) is used. The main contribution of this work is the development of a variety of controller design methods, which together with suitable parameter identification techniques provide tools for rapid prototyping for real time control of processes within the laboratory, in preparation for industrial implementation of more complex schemes. The novelty of this work lies in the use of model predictive control (MPC) methods based on a non-minimal state space formulation, which permits the inclusion of process measurements and actuations in the state vector, leading to controller designs which are immediately ready for on-line implementation. A linear MPC controller based on a non-minimal state space model is based on an approximate linear model. The results from simulation and online experiment show that the linear MPC controller realizes a satisfying reference tracking in the face of nonlinearity and time-delay. In the following chapter, a nonlinear Hammerstein model is identified, which is a type of reliable structure for describing nonlinear plants. A nonlinear MPC scheme is developed based on the Hammerstein model. An inversion block is created to cancel the effect of the nonlinearity. The performance IS also tested in both simulation and experiment. Finally, MPC is combined with sliding mode control. The non-minimal state space model is also used here. In the first part of this chapter, the idea underlying sliding mode control contributes a method of modifying the definition of the cost function in MPC. In the second half, MPC is used to design the switching surface in sliding mode control. The performance of tests on the example (ball and beam system) illustrates that these are both valid methods for dealing with complex processes.
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Allison, Timothy Charles. "System Identification via the Proper Orthogonal Decomposition." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29424.

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Although the finite element method is often applied to analyze the dynamics of structures, its application to large, complex structures can be time-consuming and errors in the modeling process may negatively affect the accuracy of analyses based on the model. System identification techniques attempt to circumvent these problems by using experimental response data to characterize or identify a system. However, identification of structures that are time-varying or nonlinear is problematic because the available methods generally require prior understanding about the equations of motion for the system. Nonlinear system identification techniques are generally only applicable to nonlinearities where the functional form of the nonlinearity is known and a general nonlinear system identification theory is not available as is the case with linear theory. Linear time-varying identification methods have been proposed for application to nonlinear systems, but methods for general time-varying systems where the form of the time variance is unknown have only been available for single-input single-output models. This dissertation presents several general linear time-varying methods for multiple-input multiple-output systems where the form of the time variance is entirely unknown. The methods use the proper orthogonal decomposition of measured response data combined with linear system theory to construct a model for predicting the response of an arbitrary linear or nonlinear system without any knowledge of the equations of motion. Separate methods are derived for predicting responses to initial displacements, initial velocities, and forcing functions. Some methods require only one data set but only promise accurate solutions for linear, time-invariant systems that are lightly damped and have a mass matrix proportional to the identity matrix. Other methods use multiple data sets and are valid for general time-varying systems. The proposed methods are applied to linear time-invariant, time-varying, and nonlinear systems via numerical examples and experiments and the factors affecting the accuracy of the methods are discussed.
Ph. D.
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Raptis, Ioannis A. "Linear and Nonlinear Control of Unmanned Rotorcraft." Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/3482.

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The main characteristic attribute of the rotorcraft is the use of rotary wings to produce the thrust force necessary for motion. Therefore, rotorcraft have an advantage relative to fixed wing aircraft because they do not require any relative velocity to produce aerodynamic forces. Rotorcraft have been used in a wide range of missions of civilian and military applications. Particular interest has been concentrated in applications related to search and rescue in environments that impose restrictions to human presence and interference. The main representative of the rotorcraft family is the helicopter. Small scale helicopters retain all the flight characteristics and physical principles of their full scale counterpart. In addition, they are naturally more agile and dexterous compared to full scale helicopters. Their flight capabilities, reduced size and cost have monopolized the attention of the Unmanned Aerial Vehicles research community for the development of low cost and efficient autonomous flight platforms. Helicopters are highly nonlinear systems with significant dynamic coupling. In general, they are considered to be much more unstable than fixed wing aircraft and constant control must be sustained at all times. The goal of this dissertation is to investigate the challenging design problem of autonomous flight controllers for small scale helicopters. A typical flight control system is composed of a mathematical algorithm that produces the appropriate command signals required to perform autonomous flight. Modern control techniques are model based, since the controller architecture depends on the dynamic description of the system to be controlled. This principle applies to the helicopter as well, therefore, the flight control problem is tightly connected with the helicopter modeling. The helicopter dynamics can be represented by both linear and nonlinear models of ordinary differential equations. Theoretically, the validity of the linear models is restricted in a certain region around a specific operating point. Contrary, nonlinear models provide a global description of the helicopter dynamics. This work proposes several detailed control designs based on both dynamic representations of small scale helicopters. The controller objective is for the helicopter to autonomously track predefined position (or velocity) and heading reference trajectories. The controllers performance is evaluated using X-Plane, a realistic and commercially available flight simulator.
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Ling, Xiaolin. "Linear and nonlinear time domain system identification at element level for structural systems with unknown excitation." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284163.

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Three time domain system identification (SI) approaches, i.e., Modified Iterative Least Square with Unknown Input (ILS-UI), Localized Structural Identification, and Modified Iterative Least Square--Extended Kalman Filter with Unknown Input (ILS-EKF-UI), are proposed to identify defects at the element level of structures. In all these methods, structures are modeled using the finite element method (FEM) and the structural parameters (stiffness and damping) are identified using only output response measurements without using any information on input excitation. Excitations are identified as a byproduct of the SI procedures. If damping is considered to be proportional or Rayleigh-type, the time domain SI technique becomes nonlinear even though the dynamic system remains linear. The Modified ILS-UI approach is essentially a nonlinear SI algorithm. The Localized Structural Identification combines a time domain SI technique and FEM formulation representing a part of the structure. The time domain responses at each time instance represent an equilibrium status of the system which is reflected in the nodal equilibrium in the FEM. Using the Localized Structural Model, only dynamic responses at the local region closely connected to the part of the structure to be identified are required. This dramatically reduces the measurement requirements, and makes it possible to identify the parameters of the whole structure by identifying only part of it. This study discusses how to select elements of the local structure and how to determine the locations and number of the output measurements. The Modified ILS-EKF-UI approach was developed by combining the Modified ILS-UI and the Localized Structural Identification. Using the Modified ILS-EKF-UI approach, the system can be identified using responses at a reduced number of dynamic degrees of freedom. This method allows the finite element mesh to be refined further for more localized parameter identification without additional response information. All three methods are verified using numerical examples. They identify the structures very well. They are found to be more accurate than other methods currently reported in the literature even when input excitation information is used to identify structures. Various types of structures are examined, including shear buildings, plane frames, and plane trusses. The proposed methods are found to be robust even when the responses are contaminated with noise.
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Vakakis, Alexander F. Caughey Thomas Kirk. "Analysis and identification of linear and nonlinear normal modes in vibrating systems /." Diss., Pasadena, Calif. : California Institute of Technology, 1991. http://resolver.caltech.edu/CaltechETD:etd-08232004-105610.

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Cieza, Aguirre Oscar Benjamín. "Rapid continuous-time identification of linear and nonlinear systems using modulation function approaches." Master's thesis, Pontificia Universidad Católica del Perú, 2015. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8123.

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At the present, system identification through modulation functions has a wide range of methods. Many of them have reached maturity levels that surpass customary Kalmanfilter approaches for discrete-time identification. In this thesis, the modulation function technique is analyzed in view of its real-time capability, as well as the possible unification of the modulation function methods based on the frequency spectrum, and ability to deal with nonlinearities. Besides, to increase the rate of convergence, the optimal parameter estimation with constraints of Byrski et al. [BFN03] is applied on integrable and convolvable systems. Furthermore, the modulated white Gaussian noise influence on linear systems is examined. The proposed methods together with the Loab-Cahen modulation functions are compared in performance for linear and convolvable systems concerning three different inputs, three normalizations, identification parameters and computational cost.
Tesis
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Books on the topic "Linear and Nonlinear System identification"

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Billings, S. A. Piecewise linear identification of nonlinear systems. Sheffield: University,Dept. of Control Engineering, 1986.

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Santos, Paulo Lopes dos. Linear parameter-varying system identification: New developments and trends. Singapore: World Scientific, 2012.

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Coca, D. Continuous-time system identification for linear and nonlinear systems using wavelet decomposition. Sheffield: University of Sheffield, Department of Automatic Control and Systems Engineering, 1996.

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Tsang, K. M. Identification of multi-class linear and nonlinear systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

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Li, L. M. Continuous time linear and nonlinear system identification in the frequency domain. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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Prakriya, Shankar. Blind identification of linear and nonlinear systems with cycloststionary inputs. Ottawa: National Library of Canada, 1993.

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Bendat, Julius S. Nonlinear system analysis and identification from random data. New York: Wiley, 1990.

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United States. National Aeronautics and Space Administration., ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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Center, Langley Research, ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Billings, Stephen A. Nonlinear System Identification. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118535561.

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Book chapters on the topic "Linear and Nonlinear System identification"

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Nelles, Oliver. "Linear Optimization." In Nonlinear System Identification, 35–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_3.

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Nelles, Oliver. "Linear Optimization." In Nonlinear System Identification, 35–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_3.

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Nelles, Oliver. "Linear Dynamic System Identification." In Nonlinear System Identification, 457–546. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_14.

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Nelles, Oliver. "Linear Dynamic System Identification." In Nonlinear System Identification, 715–830. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_18.

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Nelles, Oliver. "Local Linear Neuro-Fuzzy Models: Fundamentals." In Nonlinear System Identification, 341–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_12.

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Nelles, Oliver. "Dynamic Local Linear Neuro-Fuzzy Models." In Nonlinear System Identification, 601–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_18.

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Nelles, Oliver. "Local Linear Neuro-Fuzzy Models: Fundamentals." In Nonlinear System Identification, 393–445. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_13.

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Nelles, Oliver. "Dynamic Local Linear Neuro-Fuzzy Models." In Nonlinear System Identification, 919–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_22.

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Nelles, Oliver. "Local Linear Neuro-Fuzzy Models: Advanced Aspects." In Nonlinear System Identification, 391–449. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_13.

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Nelles, Oliver. "Linear, Polynomial, and Look-Up Table Models." In Nonlinear System Identification, 219–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_9.

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Conference papers on the topic "Linear and Nonlinear System identification"

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Mandic, D. P., and J. A. Chambers. "Advanced PRNN based nonlinear prediction/system identification." In IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980446.

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Haryanto, Ade, and Keum-Shik Hong. "Multi-Linear MPC for Nonlinear Oxy-Fuel Combustion Boiler System." In Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-038.

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Cheng, Yu, and Jinglu Hu. "Nonlinear system identification based on SVR with quasi-linear kernel." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252694.

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Ghogho, M., A. K. Nandi, and A. Swami. "Identification of Volterra nonlinear systems using circular inputs." In IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980445.

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Tang, Jiong, Rajamani Doraiswami, and Chris P. Diduch. "Identification of a linear model for nonlinear systems." In 2009 IEEE International Conference on Control and Automation (ICCA). IEEE, 2009. http://dx.doi.org/10.1109/icca.2009.5410381.

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Afri, Chouaib, Vincent Andrieu, Laurent Bako, and Pascal Dufour. "Identification of linear systems with nonlinear Luenberger Observers." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171853.

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Varadarajan, Nadathur P., and Satish Nagarajaiah. "Non Linear System Identification of Offshore Floating Structures." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57161.

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Floating structures such as spar platforms are typically designed to be compliant or move with environment loadings, rather than resisting them. Hence they are designed so that there is no dynamic amplification in wave frequency response. However, higher order nonlinear effects are produced in low frequency wind excited regions, especially in Spar platforms. It is difficult to separate the nonlinear behavior of the model response from the loading using conventional methods. In this paper, Empirical Mode Decomposition and Hilbert Transform (EMD/HT) is used to identify the nonlinear response of a spar from the model test results. From the measured response the dynamic parameters are estimated as follows: 1) The multi-component response of the floating structure is decomposed into IMF components. 2) Hilbert transform of the input and the IMF signal in the time domain is done to extract the instantaneous dynamic characteristics. 3) Amplitude and frequency dependent frequency response function is used to represent the result of HT identification. The EMD method can identify any changes in system properties in real time and can be effectively used for repair and retrofit.
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Sicuranza, Giovanni L., and Alberto Carini. "Nonlinear system identification by means of mixtures of linear-in-the-parameters nonlinear filters." In 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2013. http://dx.doi.org/10.1109/ispa.2013.6703763.

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James, Sebastian, and Sean R. Anderson. "Linear System Identification of Longitudinal Vehicle Dynamics Versus Nonlinear Physical Modelling." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516756.

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Shikimori, Takashi, Hideo Muroi, and Shuichi Adachi. "A Nonlinear System Identification Method based on Local Linear PLS Method." In Intelligent Systems and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.744-026.

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Reports on the topic "Linear and Nonlinear System identification"

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Farrar, Charles R., Keith Worden, Michael D. Todd, Gyuhae Park, Jonathon Nichols, Douglas E. Adams, Matthew T. Bement, and Kevin Farinholt. Nonlinear System Identification for Damage Detection. Office of Scientific and Technical Information (OSTI), November 2007. http://dx.doi.org/10.2172/922532.

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Zhang, Xi-Cheng. DURIP-94 Gigawatt The Beam System for Linear and Nonlinear Fir Spectroscopy. Fort Belvoir, VA: Defense Technical Information Center, April 1996. http://dx.doi.org/10.21236/ada315718.

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Bergman, Lawrence A., Alexander F. Vakakis, and D. M. McFarland. Acquisition of a Scanning Laser Vibrometer System for Experimental Studies on Nonparametric Nonlinear System Identification and Aeroelastic Instability Suppression. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada565204.

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Goodson, T., Wang III, and C. H. Dispersion and Dipolar Orientational Effects on the Linear Electro-Absorption and Electro-Optic Responses in a Model Guest/Host Nonlinear Optical System. Fort Belvoir, VA: Defense Technical Information Center, July 1996. http://dx.doi.org/10.21236/ada311120.

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Altstein, Miriam, and Ronald Nachman. Rationally designed insect neuropeptide agonists and antagonists: application for the characterization of the pyrokinin/Pban mechanisms of action in insects. United States Department of Agriculture, October 2006. http://dx.doi.org/10.32747/2006.7587235.bard.

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The general objective of this BARD project focused on rationally designed insect neuropeptide (NP) agonists and antagonists, their application for the characterization of the mechanisms of action of the pyrokinin/PBAN (PK-PBAN) family and the development of biostable, bioavailable versions that can provide the basis for development of novel, environmentally-friendly pest insect control agents. The specific objectives of the study, as originally proposed, were to: (i) Test stimulatory potencies of rationally designed backbone cyclic (BBC) peptides on pheromonotropic, melanotropic, myotropic and pupariation activities; (ii) Test the inhibitory potencies of the BBC compounds on the above activities evoked either by synthetic peptides (PBAN, LPK, myotropin and pheromonotropin) or by the natural endogenous mechanism; (iii) Determine the bioavailability of the most potent BBC compounds that will be found in (ii); (iv) Design, synthesize and examine novel PK/PBAN analogs with enhanced bioavailability and receptor binding; (v) Design and synthesize ‘magic bullet’ analogs and examine their ability to selectively kill cells expressing the PK/PBAN receptor. To achieve these goals the agonistic and antagonistic activities/properties of rationally designed linear and BBC neuropeptide (NP) were thoroughly studied and the information obtained was further used for the design and synthesis of improved compounds toward the design of an insecticide prototype. The study revealed important information on the structure activity relationship (SAR) of agonistic/antagonistic peptides, including definitive identification of the orientation of the Pro residue as trans for agonist activity in 4 PK/PBANbioassays (pheromonotropic, pupariation, melanotropic, & hindgut contractile) and a PK-related CAP₂b bioassay (diuretic); indications that led to the identification of a novel scaffold to develop biostbiostable, bioavailable peptidomimetic PK/PBANagonists/antagonists. The work led to the development of an arsenal of PK/PBAN antagonists with a variety of selectivity profiles; whether between different PKbioassays, or within the same bioassay between different natural elicitors. Examples include selective and non-selective BBC and novel amphiphilic PK pheromonotropic and melanotropic antagonists some of which are capable of penetrating the moth cuticle in efficacious quantities. One of the latter analog group demonstrated unprecedented versatility in its ability to antagonize a broad spectrum of pheromonotropic elicitors. A novel, transPro mimetic motif was proposed & used to develop a strong, selective PK agonist of the melanotropic bioassay in moths. The first antagonist (pure) of PK-related CAP₂b diuresis in flies was developed using a cisPro mimetic motif; an indication that while a transPro orientation is associated with receptor agonism, a cisPro orientation is linked with an antagonist interaction. A novel, biostablePK analog, incorporating β-amino acids at key peptidase-susceptible sites, exhibited in vivo pheromonotropic activity that by far exceeded that of PBAN when applied topically. Direct analysis of neural tissue by state-of-the-art MALDI-TOF/TOF mass spectrometry was used to identify specific PK/PK-related peptides native to eight arthropod pest species [house (M. domestica), stable (S. calcitrans), horn (H. irritans) & flesh (N. bullata) flies; Southern cattle fever tick (B. microplus), European tick (I. ricinus), yellow fever mosquito (A. aegypti), & Southern Green Stink Bug (N. viridula)]; including the unprecedented identification of mass-identical Leu/Ile residues and the first identification of NPs from a tick or the CNS of Hemiptera. Evidence was obtained for the selection of Neb-PK-2 as the primary pupariation factor of the flesh fly (N. bullata) among native PK/PK-related candidates. The peptidomic techniques were also used to map the location of PK/PK-related NP in the nervous system of the model fly D. melanogaster. Knowledge of specific PK sequences can aid in the future design of species specific (or non-specific) NP agonists/antagonists. In addition, the study led to the first cloning of a PK/PBAN receptor from insect larvae (S. littoralis), providing the basis for SAR analysis for the future design of 2ⁿᵈgeneration selective and/or nonselective agonists/antagonists. Development of a microplate ligand binding assay using the PK/PBAN pheromone gland receptor was also carried out. The assay will enable screening, including high throughput, of various libraries (chemical, molecular & natural product) for the discovery of receptor specific agonists/antagonists. In summary, the body of work achieves several key milestones and brings us significantly closer to the development of novel, environmentally friendly pest insect management agents based on insect PK/PBANNPs capable of disrupting critical NP-regulated functions.
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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani, and S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. The bounds of the area where earthquakes were located were between 55.5°N-60.0°N and 119.8°W-123.5°W. The earthquakes in the catalogue were initially detected by machine learning models, then reviewed by an analyst to confirm correct identification, and finally located using the Non-Linear Location (NonLinLoc) algorithm. Two distinct sub-areas within the bounds consider different periods to supplement what was not covered in previously published reports - the Northern Montney Trend (NMT) is covered from 2017 to 2020 while the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA) is covered from 2019 to 2020. The two sub-areas are distinguished by the BC Oil &amp; Gas Commission (BCOGC) due to differences in their geographic location and geology. The catalogue was produced by picking arrival phases on continuous seismic waveforms from 51 stations operated by various organizations in the region. A total of 17,908 events passed our quality control criteria and are included in the final catalogue. Comparably, the routine Canadian National Seismograph Network (CNSN) catalogue reports 207 seismic events - all events in the CNSN catalogue are present in our catalogue. Our catalogue benefits from the use of enhanced station coverage and improved methodology. The total number of events in our catalogue in 2017, 2018, 2019, and 2020 were 62, 47, 9579 and 8220, respectively. The first two years correspond to seismicity in the NMT where poor station coverage makes it difficult to detect small magnitude events. The magnitude of completeness within the KSMMA (ML = ~0.7) is significantly smaller than that obtained for the NMT (ML = ~1.4). The new catalogue is released with separate files for origins, arrivals, and magnitudes which can be joined using the unique ID assigned to each event.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Wu, Yingjie, Selim Gunay, and Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ytgv8834.

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Bridges often serve as key links in local and national transportation networks. Bridge closures can result in severe costs, not only in the form of repair or replacement, but also in the form of economic losses related to medium- and long-term interruption of businesses and disruption to surrounding communities. In addition, continuous functionality of bridges is very important after any seismic event for emergency response and recovery purposes. Considering the importance of these structures, the associated structural design philosophy is shifting from collapse prevention to maintaining functionality in the aftermath of moderate to strong earthquakes, referred to as “resiliency” in earthquake engineering research. Moreover, the associated construction philosophy is being modernized with the utilization of accelerated bridge construction (ABC) techniques, which strive to reduce the impact of construction on traffic, society, economy and on-site safety. This report presents two bridge systems that target the aforementioned issues. A study that combined numerical and experimental research was undertaken to characterize the seismic performance of these bridge systems. The first part of the study focuses on the structural system-level response of highway bridges that incorporate a class of innovative connecting devices called the “V-connector,”, which can be used to connect two components in a structural system, e.g., the column and the bridge deck, or the column and its foundation. This device, designed by ACII, Inc., results in an isolation surface at the connection plane via a connector rod placed in a V-shaped tube that is embedded into the concrete. Energy dissipation is provided by friction between a special washer located around the V-shaped tube and a top plate. Because of the period elongation due to the isolation layer and the limited amount of force transferred by the relatively flexible connector rod, bridge columns are protected from experiencing damage, thus leading to improved seismic behavior. The V-connector system also facilitates the ABC by allowing on-site assembly of prefabricated structural parts including those of the V-connector. A single-column, two-span highway bridge located in Northern California was used for the proof-of-concept of the proposed V-connector protective system. The V-connector was designed to result in an elastic bridge response based on nonlinear dynamic analyses of the bridge model with the V-connector. Accordingly, a one-third scale V-connector was fabricated based on a set of selected design parameters. A quasi-static cyclic test was first conducted to characterize the force-displacement relationship of the V-connector, followed by a hybrid simulation (HS) test in the longitudinal direction of the bridge to verify the intended linear elastic response of the bridge system. In the HS test, all bridge components were analytically modeled except for the V-connector, which was simulated as the experimental substructure in a specially designed and constructed test setup. Linear elastic bridge response was confirmed according to the HS results. The response of the bridge with the V-connector was compared against that of the as-built bridge without the V-connector, which experienced significant column damage. These results justified the effectiveness of this innovative device. The second part of the study presents the HS test conducted on a one-third scale two-column bridge bent with self-centering columns (broadly defined as “resilient columns” in this study) to reduce (or ultimately eliminate) any residual drifts. The comparison of the HS test with a previously conducted shaking table test on an identical bridge bent is one of the highlights of this study. The concept of resiliency was incorporated in the design of the bridge bent columns characterized by a well-balanced combination of self-centering, rocking, and energy-dissipating mechanisms. This combination is expected to lead to minimum damage and low levels of residual drifts. The ABC is achieved by utilizing precast columns and end members (cap beam and foundation) through an innovative socket connection. In order to conduct the HS test, a new hybrid simulation system (HSS) was developed, utilizing commonly available software and hardware components in most structural laboratories including: a computational platform using Matlab/Simulink [MathWorks 2015], an interface hardware/software platform dSPACE [2017], and MTS controllers and data acquisition (DAQ) system for the utilized actuators and sensors. Proper operation of the HSS was verified using a trial run without the test specimen before the actual HS test. In the conducted HS test, the two-column bridge bent was simulated as the experimental substructure while modeling the horizontal and vertical inertia masses and corresponding mass proportional damping in the computer. The same ground motions from the shaking table test, consisting of one horizontal component and the vertical component, were applied as input excitations to the equations of motion in the HS. Good matching was obtained between the shaking table and the HS test results, demonstrating the appropriateness of the defined governing equations of motion and the employed damping model, in addition to the reliability of the developed HSS with minimum simulation errors. The small residual drifts and the minimum level of structural damage at large peak drift levels demonstrated the superior seismic response of the innovative design of the bridge bent with self-centering columns. The reliability of the developed HS approach motivated performing a follow-up HS study focusing on the transverse direction of the bridge, where the entire two-span bridge deck and its abutments represented the computational substructure, while the two-column bridge bent was the physical substructure. This investigation was effective in shedding light on the system-level performance of the entire bridge system that incorporated innovative bridge bent design beyond what can be achieved via shaking table tests, which are usually limited by large-scale bridge system testing capacities.
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