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Journal articles on the topic 'Parameter-driven systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Priyadarshini, Pallavi, Fengqiong Qin, Ee-Peng Lim, and Wee-Keong Ng. "Parameter driven synthetic web database generation." Journal of Systems and Software 69, no. 1-2 (January 2004): 29–42. http://dx.doi.org/10.1016/s0164-1212(03)00002-5.

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2

Verhoek, Chris, Hossam S. Abbas, Roland Tóth, and Sofie Haesaert. "Data-Driven Predictive Control for Linear Parameter-Varying Systems." IFAC-PapersOnLine 54, no. 8 (2021): 101–8. http://dx.doi.org/10.1016/j.ifacol.2021.08.588.

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3

Poulain, Stéphane, Ophélie Arnaud, Sachi Kato, Iris Chen, Hiro Ishida, Piero Carninci, and Charles Plessy. "Machine-driven parameter screen of biochemical reactions." Nucleic Acids Research 48, no. 7 (February 6, 2020): e37-e37. http://dx.doi.org/10.1093/nar/gkaa079.

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Abstract The development of complex methods in molecular biology is a laborious, costly, iterative and often intuition-bound process where optima are sought in a multidimensional parameter space through step-by-step optimizations. The difficulty of miniaturizing reactions under the microliter volumes usually handled in multiwell plates by robots, plus the cost of the experiments, limit the number of parameters and the dynamic ranges that can be explored. Nevertheless, because of non-linearities of the response of biochemical systems to their reagent concentrations, broad dynamic ranges are necessary. Here we use a high-performance nanoliter handling platform and computer generation of liquid transfer programs to explore in quadruplicates 648 combinations of 4 parameters of a biochemical reaction, the reverse-transcription, which lead us to uncover non-linear responses, parameter interactions and novel mechanistic insights. With the increased availability of computer-driven laboratory platforms for biotechnology, our results demonstrate the feasibility and advantage of methods development based on reproducible, computer-aided exhaustive characterization of biochemical systems.

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4

Luo, Biao, Tingwen Huang, Huai-Ning Wu, and Xiong Yang. "Data-Driven $H_\infty$ Control for Nonlinear Distributed Parameter Systems." IEEE Transactions on Neural Networks and Learning Systems 26, no. 11 (November 2015): 2949–61. http://dx.doi.org/10.1109/tnnls.2015.2461023.

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5

Verdeny, Albert, Joaquim Puig, and Florian Mintert. "Quasi-Periodically Driven Quantum Systems." Zeitschrift für Naturforschung A 71, no. 10 (October 1, 2016): 897–907. http://dx.doi.org/10.1515/zna-2016-0079.

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AbstractFloquet theory provides rigorous foundations for the theory of periodically driven quantum systems. In the case of non-periodic driving, however, the situation is not so well understood. Here, we provide a critical review of the theoretical framework developed for quasi-periodically driven quantum systems. Although the theoretical footing is still under development, we argue that quasi-periodically driven quantum systems can be treated with generalisations of Floquet theory in suitable parameter regimes. Moreover, we provide a generalisation of the Floquet-Magnus expansion and argue that quasi-periodic driving offers a promising route for quantum simulations.

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6

Diop, Mamadou A., and María J. Garrido-Atienza. "Retarded evolution systems driven by fractional Brownian motion with Hurst parameter." Nonlinear Analysis: Theory, Methods & Applications 97 (March 2014): 15–29. http://dx.doi.org/10.1016/j.na.2013.11.008.

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7

Ge, Xiaobiao, Zhong Luo, Ying Ma, Haopeng Liu, and Yunpeng Zhu. "A novel data-driven model based parameter estimation of nonlinear systems." Journal of Sound and Vibration 453 (August 2019): 188–200. http://dx.doi.org/10.1016/j.jsv.2019.04.024.

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8

Sroczynski, David W., Or Yair, Ronen Talmon, and Ioannis G. Kevrekidis. "Data-driven Evolution Equation Reconstruction for Parameter-Dependent Nonlinear Dynamical Systems." Israel Journal of Chemistry 58, no. 6-7 (April 6, 2018): 787–94. http://dx.doi.org/10.1002/ijch.201700147.

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9

Li, Guo-Hui. "Synchronization of chaotic systems with parameter driven by a chaotic signal." Chaos, Solitons & Fractals 26, no. 5 (December 2005): 1485–89. http://dx.doi.org/10.1016/j.chaos.2005.04.012.

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10

Zhao, Y. M., W. F. Xie, and X. W. Tu. "Performance-based parameter tuning method of model-driven PID control systems." ISA Transactions 51, no. 3 (May 2012): 393–99. http://dx.doi.org/10.1016/j.isatra.2012.02.005.

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11

Ohnishi, Yoshihiro, Takuya Kinoshita, and Toru Yamamoto. "Performance Monitoring and Data Driven PID Parameter Tuning for Noisy Systems." IFAC-PapersOnLine 52, no. 29 (2019): 228–32. http://dx.doi.org/10.1016/j.ifacol.2019.12.654.

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12

Zhang, Rong, Manfeng Hu, and Zhenyuan Xu. "Impulsive synchronization of Rössler systems with parameter driven by an external signal." Physics Letters A 364, no. 3-4 (April 2007): 239–43. http://dx.doi.org/10.1016/j.physleta.2006.12.010.

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13

Nishida, Gou, and Noboru Sakamoto. "Data-Driven Nonlinear Optimal Control for Distributed Parameter Systems with Output Delay." IFAC-PapersOnLine 51, no. 3 (2018): 101–6. http://dx.doi.org/10.1016/j.ifacol.2018.06.028.

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14

Wakitani, Shin, Toru Yamamoto, and Takao Sato. "Design of a Performance-driven One-parameter Tuning Controller." International Journal of Control, Automation and Systems 18, no. 6 (January 22, 2020): 1453–64. http://dx.doi.org/10.1007/s12555-018-0054-5.

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15

Stennikov, V. A., E. A. Barakhtenko, and D. V. Sokolov. "Model-Driven Engineering in the Software for Parameter Optimization of Heat Supply Systems." PROGRAMMNAYA INGENERIA 7, no. 2 (February 2016): 108–16. http://dx.doi.org/10.17587/prin.7.108-116.

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16

Xie, Weiguo, Ioannis Bonis, and Constantinos Theodoropoulos. "Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems." Journal of Process Control 35 (November 2015): 50–58. http://dx.doi.org/10.1016/j.jprocont.2015.07.009.

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17

CASADO-PASCUAL, J., J. GÓMEZ-ORDÓÑEZ, M. MORILLO, and P. HÄNGGI. "CHECKING LINEAR RESPONSE THEORY IN DRIVEN BISTABLE SYSTEMS." Fluctuation and Noise Letters 02, no. 03 (September 2002): L127—L138. http://dx.doi.org/10.1142/s0219477502000737.

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The validity of linear response theory to describe the response of a nonlinear stochastic system driven by an external periodical time dependent force is put to a critical test. A variety of numerical and analytical approximations is used to compare its predictions with numerical solutions over an extended parameter regime of driving amplitudes and frequencies and noise strengths. The relevance of the driving frequency and the noise value for the applicability of linear response theory is explored for single and multi-frequency input signals.

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18

Nalluri, Madhu Sudana Rao, Kannan K, Xiao-Zhi Gao, Swaminathan V, and Diptendu Sinha Roy. "Parameter evolution of the classifiers for disease diagnosis with offline data-driven hybrid systems." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1365–84. http://dx.doi.org/10.3233/ida-194687.

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Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.

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19

Jiang, Zhengxian, Baotong Cui, Wei Wu, and Bo Zhuang. "Event-driven observer-based control for distributed parameter systems using mobile sensor and actuator." Computers & Mathematics with Applications 72, no. 12 (December 2016): 2854–64. http://dx.doi.org/10.1016/j.camwa.2016.10.009.

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20

Kirkwood, Thomas B. L. "Systems biology of ageing and longevity." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1561 (January 12, 2011): 64–70. http://dx.doi.org/10.1098/rstb.2010.0275.

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Ageing is intrinsically complex, being driven by multiple causal mechanisms. Each mechanism tends to be partially supported by data indicating that it has a role in the overall cellular and molecular pathways underlying the ageing process. However, the magnitude of this role is usually modest. The systems biology approach combines (i) data-driven modelling, often using the large volumes of data generated by functional genomics technologies, and (ii) hypothesis-driven experimental studies to investigate causal pathways and identify their parameter values in an unusually quantitative manner, which enables the contributions of individual mechanisms and their interactions to be better understood, and allows for the design of experiments explicitly to test the complex predictions arising from such models. A clear example of the success of the systems biology approach in unravelling the complexity of ageing can be seen in recent studies on cell replicative senescence, revealing interactions between mitochondrial dysfunction, telomere erosion and DNA damage. An important challenge also exists in connecting the network of (random) damage-driven proximate mechanisms of ageing with the higher level (genetically specified) signalling pathways that influence longevity. This connection is informed by actions of natural selection on the determinants of ageing and longevity.

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21

Goel, Ankit, and Dennis S. Bernstein. "Gradient-, Ensemble-, and Adjoint-Free Data-Driven Parameter Estimation." Journal of Guidance, Control, and Dynamics 42, no. 8 (August 2019): 1743–54. http://dx.doi.org/10.2514/1.g004158.

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22

Jin, Danqi, Jie Chen, Cédric Richard, and Jingdong Chen. "Model-driven online parameter adjustment for zero-attracting LMS." Signal Processing 152 (November 2018): 373–83. http://dx.doi.org/10.1016/j.sigpro.2018.06.020.

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23

Lee, Y., and J. M. Tien. "Strategic parameter-driven routing models for multidestination traffic in telecommunication networks." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 31, no. 5 (2001): 720–28. http://dx.doi.org/10.1109/3477.956033.

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24

GU, ANHUI. "RANDOM ATTRACTORS OF STOCHASTIC LATTICE DYNAMICAL SYSTEMS DRIVEN BY FRACTIONAL BROWNIAN MOTIONS." International Journal of Bifurcation and Chaos 23, no. 03 (March 2013): 1350041. http://dx.doi.org/10.1142/s0218127413500417.

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This paper is devoted to consider stochastic lattice dynamical systems (SLDS) driven by fractional Brownian motions with Hurst parameter bigger than 1/2. Under usual dissipativity conditions these SLDS are shown to generate a random dynamical system for which the existence and uniqueness of a random attractor are established. Furthermore, the random attractor is, in fact, a singleton sets random attractor.

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25

Peherstorfer, Benjamin, and Karen Willcox. "Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems." Procedia Computer Science 51 (2015): 2553–62. http://dx.doi.org/10.1016/j.procs.2015.05.363.

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26

Du, Xiu-Li, Jin-Guan Lin, Guo-Xiang Liu, and Xiu-Qing Zhou. "A physical parameter identification method of Lévy-driven vibratory systems based on multipower variation processes." Journal of Sound and Vibration 343 (May 2015): 216–29. http://dx.doi.org/10.1016/j.jsv.2015.01.012.

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27

Mu, Wen-Ying, Bao-Tong Cui, Xu-Yang Lou, and Wen Li. "Optimal switching policy for performance enhancement of distributed parameter systems based on event-driven control." Chinese Physics B 23, no. 7 (July 2014): 070204. http://dx.doi.org/10.1088/1674-1056/23/7/070204.

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28

Aggelogiannaki, Eleni, and Haralambos Sarimveis. "Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models." Computers & Chemical Engineering 32, no. 6 (June 2008): 1225–37. http://dx.doi.org/10.1016/j.compchemeng.2007.05.002.

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29

Wu, Huai-Ning, and Zhou-yang Liu. "Data-Driven Guaranteed Cost Control Design via Reinforcement Learning for Linear Systems With Parameter Uncertainties." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 11 (November 2020): 4151–59. http://dx.doi.org/10.1109/tsmc.2019.2931332.

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30

Raja, Muhammad Asif Zahoor, Muhammad Anwaar Manzar, Fiaz Hussain Shah, and Fazal Hakim Shah. "Intelligent computing for Mathieu’s systems for parameter excitation, vertically driven pendulum and dusty plasma models." Applied Soft Computing 62 (January 2018): 359–72. http://dx.doi.org/10.1016/j.asoc.2017.10.049.

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31

Wang, Cheng, Chuang Liu, Yuzhang Lin, and Tianshu Bi. "Day-ahead dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands." Energy 203 (July 2020): 117847. http://dx.doi.org/10.1016/j.energy.2020.117847.

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32

Deng, Zhihua, Qihong Chen, Liyan Zhang, Yi Zong, Keliang Zhou, and Zhichao Fu. "Control oriented data driven linear parameter varying model for proton exchange membrane fuel cell systems." Applied Energy 277 (November 2020): 115540. http://dx.doi.org/10.1016/j.apenergy.2020.115540.

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33

Moolenaar, Hanneke E., Johan Grasman, Frank M. Selten, and Maarten de Gee. "Testing a method for analyzing the effect of parameter change in climate driven ecological systems." Ecological Modelling 205, no. 3-4 (July 2007): 289–300. http://dx.doi.org/10.1016/j.ecolmodel.2007.02.021.

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34

Dai, Xisheng, Lanlan Liu, and Zhenping Deng. "Optimised data-driven terminal iterative learning control based on neural network for distributed parameter systems." International Journal of Automation and Control 15, no. 4/5 (2021): 463. http://dx.doi.org/10.1504/ijaac.2021.10037938.

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35

Dai, Xisheng, Lanlan Liu, and Zhenping Deng. "Optimised data-driven terminal iterative learning control based on neural network for distributed parameter systems." International Journal of Automation and Control 15, no. 4/5 (2021): 463. http://dx.doi.org/10.1504/ijaac.2021.116422.

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36

Krüger, Nils. "Variational principle for time-periodic quantum systems." Zeitschrift für Naturforschung A 75, no. 10 (October 25, 2020): 855–61. http://dx.doi.org/10.1515/zna-2020-0209.

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AbstractA variational principle enabling one to compute individual Floquet states of a periodically time-dependent quantum system is formulated, and successfully tested against the benchmark system provided by the analytically solvable model of a linearly driven harmonic oscillator. The principle is particularly well suited for tracing individual Floquet states through parameter space, and may allow one to obtain Floquet states even for very high-dimensional systems which cannot be treated by the known standard numerical methods.

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37

Rödönyi, G., R. Tóth, D. Pup, Á. Kisari, Zs Vígh, P. Kőrös, and J. Bokor. "Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous car." IFAC-PapersOnLine 54, no. 8 (2021): 20–26. http://dx.doi.org/10.1016/j.ifacol.2021.08.575.

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38

Trigano, Tom, and Yann Sepulcre. "Data-driven parameter selection for activity estimation in nuclear spectroscopy." Signal Processing 151 (October 2018): 99–106. http://dx.doi.org/10.1016/j.sigpro.2018.05.006.

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39

Papageorgiou, Nikolaos S. "Existence of optimal controls for a class of nonlinear distributed parameter systems." Bulletin of the Australian Mathematical Society 43, no. 2 (April 1991): 211–24. http://dx.doi.org/10.1017/s0004972700028975.

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In this paper we examine a Lagrange optimal control problem driven by a nonlinear evolution equation involving a nonmonotone, state dependent perturbation term. For this problem we establish the existence of optimal admissible pairs. For the same system we also examine a time optimal control problem involving a moving target set. Finally we work out in detail an example of a strongly nonlinear parabolic distributed parameter system.

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40

Yuan, Shenglan, Jianyu Hu, Xianming Liu, and Jinqiao Duan. "Slow manifolds for dynamical systems with non-Gaussian stable Lévy noise." Analysis and Applications 17, no. 03 (May 2019): 477–511. http://dx.doi.org/10.1142/s0219530519500027.

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This work is concerned with the dynamics of a class of slow–fast stochastic dynamical systems driven by non-Gaussian stable Lévy noise with a scale parameter. Slow manifolds with exponentially tracking property are constructed, and then we eliminate the fast variables to reduce the dimensions of these stochastic dynamical systems. It is shown that as the scale parameter tends to zero, the slow manifolds converge to critical manifolds in distribution, which helps to investigate long time dynamics. The approximations of slow manifolds with error estimate in distribution are also established. Furthermore, we corroborate these results by three examples from biological sciences.

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41

Lu, Hui, Yaxian Liu, Shi Cheng, and Yuhui Shi. "Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm." Information Sciences 536 (October 2020): 25–52. http://dx.doi.org/10.1016/j.ins.2020.05.016.

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42

Beato, Valentina, Irene Sendiña-Nadal, Ingeborg Gerdes, and Harald Engel. "Coherence resonance in a chemical excitable system driven by coloured noise." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1864 (August 2, 2007): 381–95. http://dx.doi.org/10.1098/rsta.2007.2096.

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We investigate how the temporal correlation in excitable systems driven by external noise affects the coherence of the system's response. The coupling to the fluctuating environment is introduced via fluctuations of a bifurcation parameter that controls the local dynamics of the light-sensitive Belousov–Zhabotinsky reaction and of its numerical description, the Oregonator model. Both systems are brought from a highly incoherent regime to a coherent one by an appropriate choice of the correlation time and keeping noise variance constant. This effect has been found both for an Ornstein–Uhlenbeck process and for a dichotomous telegraph signal. In the latter case, we are able to connect the optimal correlation time, for which the system behaviour is most coherent, with a characteristic time scale of the system.

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43

Runggaldier, W. J., and C. Visentin. "Combined Filtering and Parameter Estimation for Discrete-time Systems Driven by Approximately White Gaussian Noise Disturbances." IFAC Proceedings Volumes 21, no. 9 (August 1988): 869–72. http://dx.doi.org/10.1016/s1474-6670(17)54838-7.

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44

Luo, Xiaosuo. "Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine." International Journal of Control, Automation and Systems 15, no. 2 (March 8, 2017): 619–26. http://dx.doi.org/10.1007/s12555-015-0480-6.

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45

ZHENG, JIAN-FENG, ZI-YOU GAO, LING-XIAO YANG, and BAI-BAI FU. "SYNCHRONIZATION BEHAVIOR IN DISCRETE SYSTEMS." International Journal of Modern Physics C 22, no. 05 (May 2011): 441–48. http://dx.doi.org/10.1142/s012918311101635x.

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This work explores the synchronization behavior in discrete systems with both discrete time and discrete state. In order to describe the self-driven function of the node’s state, a state transfer matrix is introduced. Three typical network structures (random network, small-world network and scale-free network) are numerically studied in order to investigate the impact of network structure. Simulation results show that, according to the phase synchronization index, the coupling strength is divided into four regions: the increasing region, the maximum region, the decreasing region and the oscillation region. Moreover, the size of the oscillation region seems to be changeless, independent of the network structure and a parameter describing the number of total node’s states.

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46

Huang, Weicai, Kaiming Yang, Yu Zhu, Xin Li, Haihua Mu, and Min Li. "Data-driven rational feedforward tuning: With application to an ultraprecision wafer stage." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 6 (September 16, 2019): 748–58. http://dx.doi.org/10.1177/0959651819874563.

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Rational basis functions are introduced into iterative learning control to enhance the flexibility towards nonrepeating tasks. At present, the application of rational basis functions either suffers from nonconvex optimization problem or requires the predefinition of poles, which restricts the achievable performance. In this article, a new data-driven rational feedforward tuning approach is developed, in which convex optimization is realized without predefining the poles. Specifically, the optimal parameter which eliminates the reference-induced error is directly solved using the least square method. No parametric model is involved in the parameter tuning process and the optimal parameter is estimated using the measured data. In the noisy condition, it is proved that the estimated optimal parameter is unbiased and the estimation accuracy in terms of variance is analysed. The performance of the proposed approach is tested on an ultraprecision wafer stage. The experimental results confirm that high performance is achieved using the proposed approach.

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47

Semba, Tetsuo, and Katsuhisa Furuta. "Discrete-time adaptive control using a sliding mode." Mathematical Problems in Engineering 2, no. 2 (1996): 131–42. http://dx.doi.org/10.1155/s1024123x96000270.

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Adaptive control using a sliding mode in discrete time systems is proposed as a means of achieving robustness with respect to parameter variations, fast tracking to a desired trajectory, and fast parameter convergence, without increasing the chattering of the control inputs. We first prove the stability of a system in which the control inputs consist of equivalent control driven by the adaptive control law and bounded discontinuous control. The discontinuous control driven by the sliding control law is then obtained so that the output error quickly converges to zero. Finally, the performance improvements obtained by adding the sliding mode control input are shown through computer simulations.

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48

SHRIMALI, MANISH DEV, AWADHESH PRASAD, RAM RAMASWAMY, and ULRIKE FEUDEL. "THE NATURE OF ATTRACTOR BASINS IN MULTISTABLE SYSTEMS." International Journal of Bifurcation and Chaos 18, no. 06 (June 2008): 1675–88. http://dx.doi.org/10.1142/s0218127408021269.

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In systems that exhibit multistability, namely those that have more than one coexisting attractor, the basins of attraction evolve in specific ways with the creation of each new attractor. These multiple attractors can be created via different mechanisms. When an attractor is formed via a saddle-node bifurcation, the size of its basin increases as a power-law in the bifurcation parameter. In systems with weak dissipation, the basins of low-order periodic attractors increase linearly, while those of high-order periodic attractors decay exponentially as the dissipation is increased. These general features are illustrated for autonomous as well as driven mappings. In addition, the boundaries of the basins can also change from being smooth to fractal when a new attractor appears. Transitions in the basin boundary morphology are reflected in abrupt changes in the dependence of the uncertainty exponent on the bifurcation parameter.

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49

SCHWARTZ, IRA B., and IOANA TRIANDAF. "TRACKING SUSTAINED CHAOS." International Journal of Bifurcation and Chaos 10, no. 03 (March 2000): 571–78. http://dx.doi.org/10.1142/s0218127400000384.

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Tracking unstable periodic states first introduced in [Schwartz & Triandaf, 1992] is the process of continuing unstable solutions as a systems parameter is varied in experiments. The tracked dynamical objects have been periodic saddles of well-defined finite periods. However, other saddles, such as chaotic saddles, have not been successfully "tracked," or continued. In this paper, we introduce a new yet simple method which can be used to track chaotic saddles in dynamical systems, which allows an experimentalist to sustain chaotic transients far away from crisis parameter values. The method is illustrated on a periodically driven CO 2 laser.

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50

Zelovich, Tamar, Thorsten Hansen, Zhen-Fei Liu, Jeffrey B. Neaton, Leeor Kronik, and Oded Hod. "Parameter-free driven Liouville-von Neumann approach for time-dependent electronic transport simulations in open quantum systems." Journal of Chemical Physics 146, no. 9 (March 7, 2017): 092331. http://dx.doi.org/10.1063/1.4976731.

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