Relevant bibliographies by topics / Dynamic system identification

Academic literature on the topic 'Dynamic system identification'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Dynamic system identification"

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Hollowell, William T., Walter D. Pilkey, and Edwin M. Sieveka. "System identification of dynamic structures." Finite Elements in Analysis and Design 4, no. 1 (June 1988): 65–77. http://dx.doi.org/10.1016/0168-874x(88)90024-8.

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Denno, Peter, Charles Dickerson, and Jennifer Anne Harding. "Dynamic production system identification for smart manufacturing systems." Journal of Manufacturing Systems 48 (July 2018): 192–203. http://dx.doi.org/10.1016/j.jmsy.2018.04.006.

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Alci, Musa. "New dynamic fuzzy structure and dynamic system identification." Soft Computing 10, no. 2 (April 13, 2005): 87–93. http://dx.doi.org/10.1007/s00500-004-0428-x.

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Fang, Pan, Liming Dai, Yongjun Hou, Mingjun Du, and Wang Luyou. "The Study of Identification Method for Dynamic Behavior of High-Dimensional Nonlinear System." Shock and Vibration 2019 (March 7, 2019): 1–9. http://dx.doi.org/10.1155/2019/3497410.

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The dynamic behavior of nonlinear systems can be concluded as chaos, periodicity, and the motion between chaos and periodicity; therefore, the key to study the nonlinear system is identifying dynamic behavior considering the different values of the system parameters. For the uncertainty of high-dimensional nonlinear dynamical systems, the methods for identifying the dynamics of nonlinear nonautonomous and autonomous systems are treated. In addition, the numerical methods are employed to determine the dynamic behavior and periodicity ratio of a typical hull system and Rössler dynamic system, respectively. The research findings will develop the evaluation method of dynamic characteristics for the high-dimensional nonlinear system.
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Greblicki, W., and M. Pawlak. "Dynamic system identification with order statistics." IEEE Transactions on Information Theory 40, no. 5 (1994): 1474–89. http://dx.doi.org/10.1109/18.333862.

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Yamada, T., and T. Yabuta. "Dynamic system identification using neural networks." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 1 (1993): 204–11. http://dx.doi.org/10.1109/21.214778.

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Heij, C., and W. Scherrer. "System Identification by Dynamic Factor Models." SIAM Journal on Control and Optimization 35, no. 6 (November 1997): 1924–51. http://dx.doi.org/10.1137/s0363012995282127.

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Roberts, D. E., and N. C. Hay. "Dynamic response simulation through system identification." Journal of Sound and Vibration 295, no. 3-5 (August 2006): 1017–27. http://dx.doi.org/10.1016/j.jsv.2006.02.004.

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Kong, Mingfang, Bingzhen Chen, Xiaorong He, and Shanying Hu. "Gross error identification for dynamic system." Computers & Chemical Engineering 29, no. 1 (December 2004): 191–97. http://dx.doi.org/10.1016/j.compchemeng.2004.07.008.

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Ergon, Rolf. "Dynamic system multivariate calibration by system identification methods." Modeling, Identification and Control: A Norwegian Research Bulletin 19, no. 2 (1998): 77–97. http://dx.doi.org/10.4173/mic.1998.2.2.

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Dissertations / Theses on the topic "Dynamic system identification"

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Wester, Stefan. "Dynamic system identification of a strainfield." Thesis, KTH, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105153.

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To be able to monitor forces acting on objects is important for a lot of dierent applications. In this case the object is a big steel disc and the forces to be monitored are those acting on the rim of the disc. This is done with strain gauges that register changes in the internal strain eld. This is then run through a system model that outputs the equivalent force for that strain eld. The system model is created through a static system identication consisting of a series of test pushes on the rim of the disc. This method of system identication has a series of problem mainly that it is time consuming. The thesis presents a proof-of-concept of a dynamic system identication method. Instead of pressure applied while stationary the pressure is applied by rotating the disc against another smaller steel disc and performing the system identication on this continuous data. An algorithm to use the data is tested in simulation and the results are analyzed and proven successful. Then a experiment is performed, recording data and running the algorithm. The dynamic system identication is shown to give almost equal results to the static one. The dierence can be accounted for as problems with the force measuring or that the dynamic system identication is actually more accurate than the static one. The algorithm is concluded to work and give an advantage over the old algorithm in form of the time it takes to perform it. It has a possibility to be more accurate and also to be able to identify forces in more directions than straight into the disc.
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Deng, Chuang. "System identification in dynamic positron emission tomography /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20DENG.

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Singhavilai, Thamvarit. "Identification of electric power system dynamic equivalent." Thesis, University of Strathclyde, 2011. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15647.

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Peng, Tian. "Structural system identification by dynamic observability technique." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672173.

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Structure system identification (SSI) can be classified as static and dynamic depending on the type of excitation. SSI by Observability Method (OM) using static tests was proposed and analyzed to address the observability of the estimated parameters. This mathematical approach has been used in other fields such as hydraulics, electrical, and power networks or transportation. Usually, the structural behavior of engineering structures can be identified according to dynamic characteristics such as mode shapes, natural frequencies, and damping ratios. However, the analysis of SSI by dynamic Observability Method using dynamic information is lacking. This Ph.D. thesis developed the dynamic Observability Method using masses, modal frequencies, modal deflections based on the static OM to obtain the geometrical and mechanical parameters of the structure. This thesis mainly contains three aspects of work. Firstly, in chapter 3, the development, for the first time, of constrained observability techniques (COM) for parametric estimation of structures using dynamic information such as frequencies and mode-shapes was proposed. New algorithms are introduced based on the dynamic eigenvalue equation. Two step by step examples are used to illustrate the functioning of these. Parametric expressions for the observed variables are successfully obtained,which will allow the study of the sensitivity of each of the variables in the problem and the error distribution, which is an advantage with respect to non-parametric SSI techniques. A large structure is used to validate this new application, whose structural properties can be obtained satisfactorily in either the whole or local analysis, and the results show that the required measurement set is smaller than the required for a static analysis. Chapters 4 and 5 are the applications of COM to fill the shortcomings of current research, such as the optimal SHM+SSI strategy and uncertainty quantification. Secondly, in chapter 4, the role of the SHM strategy and the SSI analysis based on the Constrained Observability Method (COM), which aims at reducing the estimation error, is discussed. A machine learning decision tool to help building the best-combined strategy of SHM and SSI that can result in the most accurate estimations of the structural properties is proposed, and the combination of COM and decision tree algorithm is used for the first time. The machine learning algorithm is based on the theory of Decision Trees. Decision trees are firstly presented to investigate the influence of the variables (layout of bridge, span length, measurement set, and weight factor in the objective function of the COM) involved in the SHM+SSI process on the error estimation in a general structure. The verification of the method with a real bridge with different levels of damage shows that the method is robust even for a high damage level, showing the SHM+SSI strategy that yields the most accurate estimation. Finally, an analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters and to provide a way to evaluate these uncertainties. This work is carried out in chapter 5. There are a large number of UQ approaches in science and engineering. It is identified that the proposed dynamic Constrained Observability Method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. A result is compared with a method based on a Bayesian approach demonstrating its applicability and correct performance through the analysis of a reinforced concrete beam.
La identificación del sistema estructural puede clasificarse como estático y dinámico según el tipo de excitación. Recientemente, se ha propuesto y analizado SSI mediante el Método de Observabilidad (OM) utilizando medidas experimentales de pruebas estáticas para abordar la observabilidad de los parámetros estimados. Este enfoque matemático se ha utilizado en otros campos como la hidráulica, la electricidad y las redes de energía o transporte. Por lo general, el comportamiento de las estructuras de ingeniería se puede identificar de acuerdo con características dinámicas como formas modales, frecuencias naturales y amortiguamiento. Sin embargo, hasta la fecha, no se han propuesto análisis de SSI por el método de observabilidad utilizando información dinámica. Esta tesis desarrolla el Método de Observabilidad Dinámico usando masas, frecuencias propias y modos de vibración para identificar los parámetros mecánicos de los elementos de una estructura. A tal fin, se desarrollan tres líneas de trabajo. En primer lugar, se propone la primera aplicación de técnicas de observabilidad restringida para la estimación paramétrica de estructuras utilizando información dinámica como frecuencias y modos de vibración. Se introducen nuevos algoritmos basados en la ecuación dinámica de valores propios. Se utilizan dos ejemplos paso a paso para ilustrar su l funcionamiento. Se obtienen con éxito expresiones paramétricas para las variables observadas, lo que permite estudiar la sensibilidad de cada una de las variables en el problema y la distribución del error, lo cual es una ventaja respecto a las técnicas SSI no paramétricas. Para la validación de esta nueva aplicación se utiliza una estructura compleja, cuyas propiedades estructurales se pueden obtener satisfactoriamente en el análisis total o local, y los resultados muestran que el conjunto de medidas requerido es menor que en el caso del análisis estático. Los capítulos 4 y 5 son las aplicaciones de COM para subsanar las deficiencias de la investigación actual, como la estrategia óptima de SHM + SSI y la cuantificación de la incertidumbre. En segundo lugar, se discute el papel que juega la estrategia SHM y el análisis SSI basado en el Método de Observabilidad Restringido (COM), con el objetivo reducir el error de estimación. Se propone una herramienta de decisión de aprendizaje automático para ayudar a construir la mejor estrategia combinada de SHM y SSI que puede resultar en estimaciones más precisas de las propiedades estructurales. Para ello, se utiliza la combinación de algoritmo COM dinámico y el método de los árboles de decisión por primera vez. Los árboles de decisión se presentan, en primer lugar, como una herramienta útil para investigar la influencia de las variables (tipología estructural del puente, longitud del vano, conjunto de medidas experimentales y pesos en la función objetivo) involucradas en el proceso SHM + SSI con el objetivo de minimizar el error en la identificación de la estructura. La verificación del método con un puente real con diferentes niveles de daño muestra que el método es robusto incluso para un nivel de daño importante, resultando en la estrategia SHM + SSI que arroja la estimación más precisa. Por último, es necesario un análisis de cuantificación de la incertidumbre (UQ) para evaluar el efecto de las incertidumbres sobre los parámetros estimados y proporcionar una forma de evaluar las incertidumbres en los parámetros identificados. Hay una gran cantidad de enfoques de UQ en ciencia e ingeniería. En primer lugar, se identifica que el Método de Observabilidad Restringido (COM) dinámico propuesto puede compensar algunas de las deficiencias de los métodos existentes. Posteriormente, el COM se utiliza para analizar un puente real. Se compara el resultado con un método existente basado, demostrando su aplicabilidad y correcto desempeño mediante la aplicación a una viga de hormigón armado. Además, se obtiene como resultado que el mejor conjunto de puntos de medición experimental dependerá de la incertidumbre epistémica incorporada en el modelo. Dado que la incertidumbre epistémica se puede eliminar a medida que aumenta el conocimiento de la estructura, la ubicación óptima de los sensores debe lograrse considerando no sólo la precisión de los mismos, sino también los modos de vibración de la estructura.
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Stiles, Peter A. "Improvement of structural dynamic models via system identification." Thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/44086.

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Proper mathematical models of structures are beneficial for designers and analysts. The accuracy of the results is essential. Therefore, verification and/or correction of the models is vital. This can be done by utilizing experimental results or other analytical solutions. There are different methods of generating the accurate mathematical models. These methods range from completely analytically derived models, completely experimentally derived models, to a combination of the two. These model generation procedures are called System Identification. Today a popular method is to create an analytical model as accurately as possible and then improve this model using experimental results. This thesis provides a review of System Identification methods as applied to vibrating structures. One simple method and three more complex methods, chosen from current engineering literature, are implemented on the computer. These methods offer the capability to correct a discrete (for example, finite element based) model through the use of experimental measurements. The validity of the methods is checked on a two degree of freedom problem, an eight degree of freedom example frequently used in the literature, and with experimentally derived vibration results of a free-free beam.
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Ge, Ma. "Structural damage detection and identification using system dynamic parameters." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2005. http://wwwlib.umi.com/cr/syr/main.

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Mao, Lei. "Frequency-based structural damage identification and dynamic system characterisation." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7945.

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This thesis studies structural dynamic system identification in a frequency-based framework. The basic consideration stems from the fact that frequencies may generally be measured with higher accuracy than other pertinent modal data such as mode shapes; however only a limited number of frequencies may be measured in the conventional context of natural frequencies. Being able to measure extra frequencies is a key to the success of a frequency-based method. The main part of the thesis is therefore organised around the involvement of the so-called artificial boundary condition (ABC) frequencies to augment the frequency dataset for general structural damage identification. In essence, the ABC frequencies correspond to the natural frequencies of the system with additional pin supports, but may be extracted from specially configured incomplete frequency response function matrix of the original structure without the need of physically imposing the additional supports. In the first part of the research, a particular focus is placed on the actual extraction of these ABC frequencies from physical experiments through effective modal testing, data collection, data processing and analysis. The influences of key processes involved in a typical modal experimental procedure, including high-fidelity measurement of the (impact) excitation input, averaging, windowing, and an effective use of post-processing techniques, particularly the Singular Value Decomposition (SVD) technique, are scrutinised in relation to the extraction of the ABC frequencies. With appropriate implementation of testing and data processing procedures, results demonstrate that all one-pin and two-pin ABC frequencies from the first few modes can be extracted with good quality in a laboratory setting, and the accuracy of extracted ABC frequencies is comparable to natural frequencies of corresponding orders. A comprehensive study is then carried out to investigate the sensitivities of ABC frequencies to damages. Two-pin ABC frequency sensitivity is formulated by extending the expression of anti-resonance sensitivity. On this basis, the mode shape contribution is adopted as a criterion for the selection of more sensitive ABC frequencies to be employed in detailed parameter identification or finite element model updating procedures. The soundness of using ABC frequencies in structural parameter identification and the effectiveness of the above ABC frequency selection method are subsequently examined through case studies involving laboratory experiments and the corresponding FE model updating. Furthermore, a preliminary study is carried out to examine the possibility of formulating ABC frequency-based damage indicator, herein with an analogy to the mode shape curvature, for direct damage assessment. As an extended investigation in the general framework of frequency-based dynamic identification, in the last part of the thesis, a complex dynamic system, namely a railway bridge under moving loads & masses, is evaluated with regard to the various frequency characteristics involved. The variation of the natural frequencies of the bridge-moving mass system, as well as the presence of the apparent frequencies from the trainloads, are analysed in detail. Besides simplified theoretical analysis, a computational model is developed to simulate the combined bridge-moving vehicle/train system, where the vehicle mass is coupled with the bridge via surface contact. The model is verified by comparison with field measurement data and theoretical predictions. Parametric studies enable a clear identification of the correlation of the frequency contents between the response and the trainload, and provide new insight into the significance of the so-called driving and dominant frequencies. It is found that much of the dynamic response phenomena, including the resonance effect, may be explained from the view point of the frequency characteristics of the trainload pattern, which is governed primarily by the ratio between the carriage length and the bridge length. Finally, a resonance severity indicator (the Z-factor) is developed for the assessment of the resonance effect in the railway bridge response when the trainload moves at a resonance speed. Numerical results demonstrate that the proposed methods are effective for the determination of the critical speed and the resonance effects, including the situations where a significant carriage mass is incorporated.
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McCormack, Anthony Sean. "The design of periodic excitations for dynamic system identification." Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/3671/.

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System identification techniques are developed for modelling linear and nonlinear systems. The main results of the work are concerned with the design and utilisation of periodic perturbation signals in general areas of time- and frequency-domain system identification. A design strategy is given for a new class of perturbation signals, together with examples of their use in system identification applications. Signal processing procedures are developed for the practical treatment of drift disturbances and transient effects, and also for the detection of nonlinear contributions to the measurement data. The techniques rely completely on the periodicity of the excitation, and so the advantageous properties of periodic input signals are considered in detail. The use of periodic excitations in discrete- and continuous-time nonlinear system identification is also reported, with the identification methods illustrating the worth of frequency-domain measurements in this area. An automatic tuning procedure for PID controllers is also developed, which illustrates an application of system identification techniques to control problems.
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Szabo, Andrew P. "System Identification and Model-Based Control of Quadcopter UAVs." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1553197265058507.

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Zaidi, Salman [Verfasser]. "System Identification of Stochastic Nonlinear Dynamic Systems using Takagi-Sugeno Fuzzy Models / Salman Zaidi." Kassel : Kassel University Press, 2019. http://d-nb.info/118450279X/34.

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Books on the topic "Dynamic system identification"

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Maine, Richard E. Identification of dynamic systems: Theory and formulation. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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McCormack, Anthony Sean. The design of periodic excitations for dynamic system identification. [s.l.]: typescript, 1995.

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Marco, Münchhof, and SpringerLink (Online service), eds. Identification of Dynamic Systems: An Introduction with Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Chen, S. Neural networks for non-linear dynamic system modelling and identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

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Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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Schenk, Axel. Modal identification of a deployable space truss. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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McEwen, Matthew D. Dynamic system identification and modeling of a rotary wing UAV for stability and control analysis. Monterey, Calif: Naval Postgraduate School, 1998.

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Maine, R. E. Identification of dynamic systems. Neuilly sur Seine: Agard, 1985.

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Isermann, Rolf, and Marco Münchhof. Identification of Dynamic Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-78879-9.

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Book chapters on the topic "Dynamic system identification"

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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. "Nonlinear Dynamic System Identification." In Nonlinear System Identification, 547–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_15.

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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. "Nonlinear Dynamic System Identification." In Nonlinear System Identification, 831–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_19.

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Nelles, Oliver. "Applications of Dynamic Models." In Nonlinear System Identification, 677–708. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_21.

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Nelles, Oliver. "Applications of Dynamic Models." In Nonlinear System Identification, 1007–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_25.

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Nelles, Oliver. "Dynamic Neural and Fuzzy Models." In Nonlinear System Identification, 587–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_17.

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Nelles, Oliver. "Dynamic Neural and Fuzzy Models." In Nonlinear System Identification, 903–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_21.

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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. "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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Conference papers on the topic "Dynamic system identification"

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Xiaoping Xu, Fang Dai, and Feng Wang. "Identification of nonlinear dynamic system." In 2011 International Conference on Electronics and Optoelectronics (ICEOE). IEEE, 2011. http://dx.doi.org/10.1109/iceoe.2011.6013432.

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Ful-Chiang Wu, Ruei-Lung Lai, Chi-Hao Yeh, and Cheng-Hsiung Chen. "Robust design of unlinearized dynamic system." In 2011 International Conference on Modelling, Identification and Control. IEEE, 2011. http://dx.doi.org/10.1109/icmic.2011.5973744.

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Ergon, R., and D. Di Ruscio. "Dynamic system calibration by system identification methods." In 1997 European Control Conference (ECC). IEEE, 1997. http://dx.doi.org/10.23919/ecc.1997.7082324.

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Koh, C. G., S. T. Quek, and K. F. Tee. "DAMAGE IDENTIFICATION OF STRUCTURAL DYNAMIC SYSTEM." In Proceedings of the Second International Conference. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776228_0116.

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Shopov, Ventseslav, and Vanya Markova. "Identification of Non-linear Dynamic System." In 2019 International Conference on Information Technologies (InfoTech). IEEE, 2019. http://dx.doi.org/10.1109/infotech.2019.8860871.

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Chen, Kaian, Zhaojian Li, Yan Wang, Jing Wang, Kai Wu, and Dimitar P. Filev. "Online Nonlinear System Identification With Parameter Constraints: Application to Automotive Engine Systems." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9092.

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Abstract In this paper, we treat the problem of online nonlinear system identification with parameter constraints. This approach is based upon our prior work on nonlinear system identification that exploits evolving Spatial-Temporal Filters (STF) to dynamically decompose system’s input/output space into a nonlinear combination of weighted local models. We extend the nonlinear system identification framework with the capability of dealing with linear equality and inequality parameter constraints. We leverage the gradient projection method in the local model parameter estimation process to inherently enforce the parameter constraints while retaining optimality. We apply the proposed algorithm to a turbo-charged gasoline engine system and promising results are demonstrated by experimental data.
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CHOU, CHAUR-MING, and CHI-HSING WU. "System identification and damage localization of dynamic structures." In Dynamics Specialists Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-1203.

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Angarita, John, Daniel Doyle, Gustavo Gargioni, and Jonathan Black. "Input Excitation Analysis for Black-Box Quadrotor Model System Identification." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3159.

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Abstract System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.
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Guanlin Wang, Jihong Zhu, Chengxu Yang, and Hui Xia. "System identification for helicopter yaw dynamic modelling." In 2011 3rd International Conference on Computer Research and Development (ICCRD). IEEE, 2011. http://dx.doi.org/10.1109/iccrd.2011.5763852.

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Dehui, Wu. "Identification of Nonlinear Dynamic System with SVR." In 2007 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.4346762.

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Reports on the topic "Dynamic system identification"

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Hebert, Anthony J., and Paul R. Mackin. Advanced Modeling and System Parameter Identification through Minimal Dynamic Stimulation and Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada609130.

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Red-Horse, J. R. Structural system identification: Structural dynamics model validation. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/469145.

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Steele, L. L., J. R. Grant, Harrold Jr., Erhart D. P., Anex J. J., and R. P. Application of System Identification Techniques to Combustor Poststall Dynamics. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada187898.

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Georgiou, Tryphon T. Aspects of Modeling, Identification and Control of Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada299411.

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Basar, Tamer. Performance-Driven Robust Identification and Control of Uncertain Dynamical Systems. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/900284.

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Bergman, Lawrence A., Alexander F. Vakakis, and D. M. McFarland. Towards Development of Nonparametric System Identification Base Based on Slow-Flow Dynamics, with Application to Damage Detection and Uncertainty Quantification. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada548212.

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Bhatt, Uma S., Renate Wackerbauer, Igor V. Polyakov, David E. Newman, and Raul E. Sanchez. Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1225814.

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8

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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Soloviev, Vladimir, Oleksandr Serdiuk, Serhiy Semerikov, and Arnold Kiv. Recurrence plot-based analysis of financial-economic crashes. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4121.

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The article considers the possibility of analyzing the dynamics of changes in the characteristics of time series obtained on the basis of recurrence plots. The possibility of using the studied indicators to determine the presence of critical phenomena in economic systems is considered. Based on the analysis of economic time series of different nature, the suitability of the studied characteristics for the identification of critical phenomena is assessed. The description of recurrence diagrams and characteristics of time series that can be obtained on their basis is given. An analysis of seven characteristics of time series, including the coefficient of self-similarity, the coefficient of predictability, entropy, laminarity, is carried out. For the entropy characteristic, several options for its calculation are considered, each of which allows the one to get its own information about the state of the economic system. The possibility of using the studied characteristics as precursors of critical phenomena in economic systems is analyzed. We have demonstrated that the entropy analysis of financial time series in phase space reveals the characteristic recurrent properties of complex systems. The recurrence entropy methodology has several advantages compared to the traditional recurrence entropy defined in the literature, namely, the correct evaluation of the chaoticity level of the signal, the weak dependence on parameters. The characteristics were studied on the basis of daily values of the Dow Jones index for the period from 1990 to 2019 and daily values of oil prices for the period from 1987 to 2019. The behavior of recurrence entropy during critical phenomena in the stock markets of the USA, Germany and France was studied separately. As a result of the study, it was determined that delay time measure, determinism and laminarity can be used as indicators of critical phenomena. It turned out that recurrence entropy, unlike other entropy indicators of complexity, is an indicator and an early precursor of crisis phenomena. The ways of further research are outlined.
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10

Priadko, Andrii O., Kateryna P. Osadcha, Vladyslav S. Kruhlyk, and Volodymyr A. Rakovych. Development of a chatbot for informing students of the schedule. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3744.

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The article describes the process of developing a chatbot to provide students with information about schedules using the Telegram mobile messenger. During the research, the following tasks have been performed: the analysis of notification systems for their use in the educational process, identification of problems of notifying students about the schedule (dynamic environment, traditional presentation of information, lack of round-the-clock access), substantiation of the choice of mobile technologies and Telegram messenger, determination of the requirements to the software, generalization of the chatbot functioning features, description of the structure, functionality of the program to get information about the schedule using a chatbot. The following tasks have been programmatically implemented: obtaining data from several pages of a spreadsheet (faculty / institute, red / green week, group number, day of the week, period number, discipline name, information about the teacher); presentation of data in a convenient form for the messenger (XML); implementation of the mechanism of convenient presentation of data in the messenger (chatbot). Using Python and the Telegram API, the software has been designed to increase students; immediacy in getting the information about the schedules, minimizing the time spent, and optimizing of planning of student activities and higher education institution functioning.
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