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Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Chaotic modes"

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Zelevinsky, Vladimir. "Chaotic dynamics and collective modes." Nuclear Physics A 649, no. 1-4 (March 1999): 403–11. http://dx.doi.org/10.1016/s0375-9474(99)00090-1.

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2

YAO, WEIGUANG, PEI YU, CHRISTOPHER ESSEX, and MATT DAVISON. "COMPETITIVE MODES AND THEIR APPLICATION." International Journal of Bifurcation and Chaos 16, no. 03 (March 2006): 497–522. http://dx.doi.org/10.1142/s0218127406014976.

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We investigate nonlinear dynamical systems from the mode competition point of view, and propose the necessary conditions for a system to be chaotic. We conjecture that a chaotic system has at least two competitive modes (CM's). For a general nonlinear dynamical system, we give a simple, dynamically motivated definition of mode suitable for this concept. Since for most chaotic systems it is difficult to obtain the form of a CM, we focus on the competition between the corresponding modulated frequency components of the CM's. Some direct applications result from the explicit form of the frequency functions. One application is to estimate parameter regimes which may lead to chaos. It is shown that chaos may be found by analyzing the frequency function of the CM's without applying a numerical integration scheme. Another application is to create new chaotic systems using custom-designed CM's. Several new chaotic systems are reported.
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3

Evano, Benjamin, François Lignières, and Bertrand Georgeot. "Regularities in the spectrum of chaotic p-modes in rapidly rotating stars." Astronomy & Astrophysics 631 (November 2019): A140. http://dx.doi.org/10.1051/0004-6361/201936459.

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Context. Interpreting the oscillations of massive and intermediate mass stars remains a challenging task. In fast rotators, the oscillation spectrum of p-modes is a superposition of sub-spectra which corresponds to different types of modes, among which island modes and chaotic modes are expected to be the most visible. This paper is focused on chaotic modes, which have not been thoroughly studied before. Aims. We study the properties of high frequency chaotic p-modes in a polytropic model. Unexpected peaks appear in the frequency autocorrelations of the spectra. Our goal is to find a physical interpretation for these peaks and also to provide an overview of the mode properties. Methods. We used the 2D oscillation code “TOP” to produce the modes and acoustic ray simulations to explore the wave properties in the asymptotic regime. Using the tools developed in the field of quantum chaos (or wave chaos), we derived an expression for the frequency autocorrelation involving the travel time of acoustic rays. Results. Chaotic mode spectra were previously thought to be irregular, that is, described only through their statistical properties. Our analysis shows the existence, in chaotic mode spectra, of a pseudo large separation. This means that chaotic modes are organized in series, such that the modes in each series follow a nearly regular frequency spacing. The pseudo large separation of chaotic modes is very close to the large separation of island modes. Its value is related to the sound speed averaged over the meridional plane of the star. In addition to the pseudo large separation, other correlations appear in the numerically calculated spectra. We explain their origin by the trapping of acoustic rays near the stable islands.
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4

Peña, M., and E. Kalnay. "Separating fast and slow modes in coupled chaotic systems." Nonlinear Processes in Geophysics 11, no. 3 (July 27, 2004): 319–27. http://dx.doi.org/10.5194/npg-11-319-2004.

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Abstract. We test a simple technique based on breeding to separate fast and slow unstable modes in coupled systems with different time scales of evolution and variable amplitudes. The technique takes advantage of the earlier saturation of error growth rate of the fastest mode and of the lower value of the saturation amplitude of perturbation of either the fast or the slow modes. These properties of the coupled system allow a physically-based selection of the rescaling time interval and the amplitude of initial perturbations in the "breeding" of unstable modes (Toth and Kalnay, 1993, 1996, 1997; Aurell et al., 1997; Boffetta et al., 1998) to isolate the desired mode. We perform tests in coupled models composed of fast and slow versions of the Lorenz (1963) model with different strengths of coupling. As examples we present first a coupled system which we denote "weather with convection", with a slow, large amplitude model coupled with a fast, small amplitude model, second an "ENSO" system with a "tropical atmosphere" strongly coupled with a "tropical ocean", and finally a triply coupled system denoted "tropical-extratropical" in which a fast model (representing the "extratropical atmosphere") is loosely coupled to the "ENSO" system. We find that it is always possible to isolate the fast modes by taking the limit of small amplitudes and short rescaling intervals, in which case, as expected, the results are the same as the local Lyapunov growth obtained with the linear tangent model. In contrast, slow modes cannot be isolated with either Lyapunov or Singular vectors, since the linear tangent and adjoint models are dominated by the fast modes. Breeding is successful in isolating slow modes if rescaling intervals and amplitudes are chosen from physically appropriate scales.
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Lindgren, Kristian, and Bengt Å. G. Månsson. "Entropy Production in a Chaotic Chemical System." Zeitschrift für Naturforschung A 41, no. 9 (September 1, 1986): 1111–17. http://dx.doi.org/10.1515/zna-1986-0904.

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The average rate of entropy production in a homogenous chemical system is investigated in oscillating periodic and chaotic modes as well as in coexisting stationary states. The simulations are based on an abstract model of a chemical reaction system with three freely varying concentrations. Five concentrations are assumed to be kept constant by suitable flows across the boundary. A fixed concentration is used as a control parameter. Second order mass action kinetics with reverse reaction is used. An unexpected result is that periodic modes in some windows in the chaotic interval have higher average rate of entropy production than the surrounding chaotic modes. A chaotic mode coexists with a stable stationary state with smaller entropy production. A unique (unstable) stationary state produces more entropy than the corresponding oscillating mode.
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6

KUSMARTSEV, FEO V., and KARL E. KÜRTEN. "CHAOTIC MODES IN SCALE FREE OPINION NETWORKS." International Journal of Modern Physics B 23, no. 20n21 (August 20, 2009): 4001–20. http://dx.doi.org/10.1142/s0217979209063225.

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In this paper, we investigate processes associated with formation of public opinion in varies directed random, scale free and small-world social networks. The important factor of the opinion formation is the existence of contrarians which were discovered by Granovetter in various social psychology experiments1,2,3 long ago and later introduced in sociophysics by Galam.4 When the density of contrarians increases the system behavior drastically changes at some critical value. At high density of contrarians the system can never arrive to a consensus state and periodically oscillates with different periods depending on specific structure of the network. At small density of the contrarians the behavior is manifold. It depends primary on the initial state of the system. If initially the majority of the population agrees with each other a state of stable majority may be easily reached. However when originally the population is divided in nearly equal parts consensus can never be reached. We model the emergence of collective decision making by considering N interacting agents, whose opinions are described by two state Ising spin variable associated with YES and NO. We show that the dynamical behaviors are very sensitive not only to the density of the contrarians but also to the network topology. We find that a phase of social chaos may arise in various dynamical processes of opinion formation in many realistic models. We compare the prediction of the theory with data describing the dynamics of the average opinion of the USA population collected on a day-by-day basis by varies media sources during the last six month before the final Obama-McCain election. The qualitative ouctome is in reasonable agreement with the prediction of our theory. In fact, the analyses of these data made within the paradigm of our theory indicates that even in this campaign there were chaotic elements where the public opinion migrated in an unpredictable chaotic way. The existence of such a phase of social chaos reflects a main feature of the human being associated with some doubts and uncertainty and especially associated with contrarians which undoubtly exist in any society.
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Cejnar, Pavel, Pavel Stránský, and Michal Macek. "Regular and Chaotic Collective Modes in Nuclei." Nuclear Physics News 21, no. 4 (October 2011): 22–27. http://dx.doi.org/10.1080/10619127.2011.629919.

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8

Hellberg, Carl S., and Steven A. Orszag. "Chaotic behavior of interacting elliptical instability modes." Physics of Fluids 31, no. 1 (January 1988): 6–8. http://dx.doi.org/10.1063/1.867010.

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9

Podvalny, S. L., and E. M. Vasiljev. "Intensification of heat transfer in chaotic modes." IOP Conference Series: Materials Science and Engineering 1035, no. 1 (January 1, 2021): 012046. http://dx.doi.org/10.1088/1757-899x/1035/1/012046.

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10

Karimov, Timur, Denis Butusov, Valery Andreev, Artur Karimov, and Aleksandra Tutueva. "Accurate Synchronization of Digital and Analog Chaotic Systems by Parameters Re-Identification." Electronics 7, no. 7 (July 20, 2018): 123. http://dx.doi.org/10.3390/electronics7070123.

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The verification of the digital models of chaotic systems and processes is a valuable problem in many practical applications, such as nonlinear control and communications. In our study, we propose a hybrid technique for chaotic systems’ identification, based on the chaotic synchronization of digital and analog counterparts and a numerical optimization method used for the fine tuning of parameters. An analog circuit implementing the Rössler oscillator with digitally controlled parameters was chosen as an identification object, and the FPGA model was used as a digital counterpart for coupling and parameter retrieval. The synchronization between analog and digital chaotic models can be used to estimate the quality of an identification procedure. The results of this study clarify the practical bounds of digital and analog systems’ equivalence. They also contribute to the problem of designing technical systems possessing advantages of both analog and digital chaotic generators (e.g., a high accuracy and protection from quasi-chaotic oscillation modes).
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Дисертації з теми "Chaotic modes"

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Halimi, Meriem. "Observation et détection de modes pour la synchronisation des systèmes chaotiques : une approche unifiée." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00942426.

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Le travail développé dans ce manuscrit porte sur la synchronisation des systèmes chaotiques. Il est articulé autour de deux axes principaux : la synthèse d'observateur et la détection de modes. Dans un premier temps, quelques rappels sur le chaos et les principales architectures de systèmes de chi ffrement chaotiques sont e ffectués. Ensuite, nous montrons comment les systèmes chaotiques à non linéarité polynomiale ou affi nes à commutation peuvent se réécrire sous forme LPV polytopique. Une revue des principaux résultats sur la synthèse d'observateurs LPV polytopiques reposant sur l'utilisation des LMI est faite. Une extension des résultats aux observateurs polytopiques à entrées inconnues, à la fois dans le cas déterministe, bruité ou incertain est proposée. Ces observateurs assurent la synchronisation du chaos et donc le déchiff rement dans les systèmes de chiff rement "modulation paramétrique", "commutation chaotique", "transmission à deux canaux" et "chiff rement par inclusion". Pour les systèmes a ffines à commutation utilisés en tant que générateur du chaos, le cas où l'état discret n'est pas accessible est considéré. Une présentation unifi ée des méthodes fondées sur les espaces de parité, proposées dans la littérature pour les systèmes linéaires et affi nes à commutation à temps discret, est réalisée. Le problème de discernabilité fait l'objet d'une étude approfondie. Une approche pour estimer les retards variables des systèmes a ffines et affi nes à commutation à temps discret, formulée en termes de détection de modes, est proposée en tant que solution à l'estimation de retard pour le chiff rement par injection de retard.
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2

Papaphilippou, Apostolos D. "Essays on chaotic macroeconomics." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387675.

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3

Cai, Qin. "Detecting Chaotic Signals with Nonlinear Models." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4564.

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In this thesis we apply chaotic dynamic data analysis to the area of discrete time signal processing. A newly developed Hidden Filter Hidden Markov Model is introduced in detection of chaotic signals. Numerical experiments have verified that this novel nonlinear model outperforms linear AR model in detecting chaotic signals buried by noise having similar power spectra. A simple Histogram Model is proposed which can also be used to do detection on the data sets with chaotic behavior. Receiver Operating Characteristics for a variety of noise levels and model classes are reported.
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4

Zhao, Yajing. "Chaotic Model Prediction with Machine Learning." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.

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Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and propose the task of predicting future states of the chaotic system from limited observations. We explore two directions, answering two distinct fundamental questions of the system based on how informed we are about the underlying model. When we know the data is generated by the Lorenz System with unknown parameters, our task becomes parameter estimation (a white-box problem), or the ``inverse'' problem. When we know nothing about the underlying model (a black-box problem), our task becomes sequence prediction. We propose two algorithms for the white-box problem: Markov-Chain-Monte-Carlo (MCMC) and a Multi-Layer-Perceptron (MLP). Specially, we propose to use the Metropolis-Hastings (MH) algorithm with an additional random walk to avoid the sampler being trapped into local energy wells. The MH algorithm achieves moderate success in predicting the $\rho$ value from the data, but fails at the other two parameters. Our simple MLP model is able to attain high accuracy in terms of the $l_2$ distance between the prediction and ground truth for $\rho$ as well, but also fails to converge satisfactorily for the remaining parameters. We use a Recurrent Neural Network (RNN) to tackle the black-box problem. We implement and experiment with several RNN architectures including Elman RNN, LSTM, and GRU and demonstrate the relative strengths and weaknesses of each of these methods. Our results demonstrate the promising role of machine learning and modern statistical data science methods in the study of chaotic dynamic systems. The code for all of our experiments can be found on \url{https://github.com/Yajing-Zhao/}
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Tsujimoto, Tsunehiro. "Calibration of the chaotic interest rate model." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/2568.

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In this thesis we establish a relationship between the Potential Approach to interest rates and the Market Models. This relationship allows us to derive the dynamics of forward LIBOR rates and forward swap rates by modelling the state price density. It means that we are able to secure the arbitrage-free condition and positive interest rate feature when we model the volatility drifts of those dynamics. On the other hand, we develop the Potential Approach, particularly the Hughston-Rafailidis Chaotic Interest Rate Model. The early argument enables us to infer that the Chaos Models belong to the Stochastic Volatility Market Models. In particular, we propose One-variable Chaos Models with the application of exponential polynomials. This maintains the generality of the Chaos Models and performs well for yield curves comparing with the Nelson-Siegel Form and the Svensson Form. Moreover, we calibrate the One-variable Chaos Model to European Caplets and European Swaptions. We show that the One-variable Chaos Models can reproduce the humped shape of the term structure of caplet volatility and also the volatility smile/skew curve. The calibration errors are small compared with the Lognormal Forward LIBOR Model, the SABR Model, traditional Short Rate Models, and other models under the Potential Approach. After the calibration, we introduce some new interest rate models under the Potential Approach. In particular, we suggest a new framework where the volatility drifts can be indirectly modelled from the short rate via the state price density.
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6

Yannacopoulos, A. N. "Diffusion models in strongly chaotic Hamiltonian systems." Thesis, University of Warwick, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357654.

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7

Jamison, Sharon Linda. "Chaotic behaviour in looped car following models." Thesis, University of Ulster, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442372.

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8

Selemani, Kamardine. "Analyse et optimisation des chambres réverbérantes à l'aide du concept de cavité chaotique ouverte." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1043/document.

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Ce travail porte sur l'optimisation de la géométrie de chambre réverbérante en s'inspirant du concept de cavité chaotique. Les chambres réverbérantes (RC) sont de plus en plus utilisées comme moyen de test de compatibilité électromagnétique. Elles sont utilisées au-delà d'une fréquence minimale à parti de laquelle les champs sont, dans le volume central de la cavité, statistiquement homogènes et isotropes ; l'obtention de ces propriétés statistiques nécessite l'utilisation d'un mécanisme de brassage, pouvant être mécanique ou électronique. Or, dans les cavités chaotiques, la plupart des modes sont associés à des champs statistiquement homogènes et isotropes, et ceci sans avoir recours à aucun brassage. C'est pourquoi un rapprochement entre chambres réverbérantes et cavités chaotiques a été fait dans ce travail.En premier lieu, nous nous intéressons à des cavités chaotiques 2D obtenues par des modifications successives d'une cavité rectangulaire. Les mesures effectuées dans ces cavités à l'aide d'une théorie perturbative, validées par des résultats de simulation, montrent qu'un champ électrique homogène est obtenu. Les principes retenus pour modifier la géométrie de la cavité rectangulaire seront repris dans les cavités 3D.Les propriétés de trois cavités 3D obtenues en modifiant une cavité parallélépipédique sont étudiées et comparées à celles d'une chambre réverbérante classique munie d'un brasseur de modes. Les modes propres et fréquences de résonance sont déterminés pour ces quatre cavités à l'aide du logiciel HFSS d'Ansoft, tout d'abord en considérant des cavités de géométrie figée, puis en y incluant un brassage mécanique.L'étude de l'homogénéité et de l'isotropie des modes propres montre clairement que les meilleures performances sont obtenues pour une des cavités chaotiques proposées, et ceci quels que soient les critères utilisés.Par ailleurs, il est montré que, dans la chambre réverbérante classique, un grand nombre de modes présente une forte localisation spatiale de l'énergie électrique, alors que ce phénomène ne se produit pas dans la cavité chaotique retenue. Ce phénomène, non détectable par les mesures classiquement effectuées en chambre réverbérante, est dommageable à l'obtention des propriétés d'homogénéité et d'isotropie requises dans le volume de travail.Enfin, l'étude de la distribution des écarts entre fréquences de résonance montre, comme prédit par la Théorie des Matrices Aléatoire, une concordance entre le suivi de la loi asymptotique prévue dans une cavité chaotique et les propriétés d'homogénéité et d'isotropie des champs. Ceci ouvre la voie vers l'utilisation de critères de caractérisation basés sur les fréquences de résonance et non plus uniquement sur les distributions des champs
This work deals with the optimization of the geometry of a reverberation chamber, drawing inspiration from the concept of chaotic cavity. Reverberation chambers, widely used for electromagnetic compatibility tests, are used above a minimal frequency from which the fields are statistically isotropic and uniform; however to respect these properties, a mode stirring process is necessary, that can be mechanical or electronic. As, in chaotic cavities, most modes are isotropic and uniform without the help of any stirring process, we take advantage of the knowledge gained from the studies of chaotic cavities to optimize reverberation chamber behavior.We firstly consider 2D chaotic cavities obtained by modifying a rectangular cavity. Measurements besed on a perturbative approch, and validated by simulations, show uniformly distributed electric fields. Similar geometrical modifications are then proposed in 3D.Three 3D different geometries of cavities obtained from a 3D rectangular cavity are then studied, and their properties are compared with those of a classical reverberation chamber equipped witdh a mode stirrer. Eigenmodes and resonant frequencies are determined numerically using Ansoft HFSS software, first by considering fixed cavity geometries, then by moving the stirrer.Electric field uniformity and isotropy are studied using several criteria; all of them clearly show that the best performances are attained within one of the proposed chaotic cavities.Moreover, a strong energy localization effect appears for numerous modes in the classical reverberation chamber, whereas it is not observed in the proposed 3D chaotic cavity. This effect, never reported in reverberation chamber studies, affects the field uniformity and isotropy within the working volume.The cavities properties are also compared width respect to their eigenfrequency spacing distributions. As predicted by the Random matrix Theory, the best agrement width the asymptotic law associated to chaotic cavities corresponds to the best field properties in terms of uniformity and isotropy. It leads to the proposal of reverberation chamber characterization criteria based on resonant frequencies instead of field distributions
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Alhawarat, Mohammad Omar Ibrahim. "Learning and memory in chaotic spiking neural models." Thesis, Oxford Brookes University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444297.

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Mizuno, Yoshinori. "Chaotic mixing in a model of static mixer." 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/145321.

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Книги з теми "Chaotic modes"

1

Skiadas, Christos H. Chaotic modelling and simulation: analysis of chaotic models, attractors and forms. Boca Raton: Chapman & Hall/CRC, 2009.

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2

Yannacopoulos, A. N. Diffusion models in strongly chaotic Hamiltonian systems. [s.l.]: typescript, 1993.

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3

Aguirre, L. A. Validating identified nonlinear models with chaotic dynamics. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1993.

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4

H, Skiadas Christos, and Dimotikalis Ioannis, eds. Chaotic systems: Theory and applications. Singapore: World Scientific, 2010.

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5

Chaotic elections!: A mathematician looks at voting. [Providence, R.I.]: American Mathematical Society, 2001.

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6

Benhabib, Jess. Chaotic interest rate rules: Expanded version. Cambridge, MA: National Bureau of Economic Research, 2004.

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7

Aguirre, L. A. Retrieving dynamical invariants from chaotic data using NARMAX models. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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8

Hommes, Carsien Harm. Chaotic dynamics in economic models: Some simple case-studies. Groningen: Wolters-Noordhoff, 1991.

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9

Hans, Dewachter, and Embrechts Marc, eds. Exchange rate theory: Chaotic models of foreign exchange markets. Oxford, UK: Blackwell, 1993.

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10

Chaotic dynamics applied to biological information processing. Berlin: Akademie-Verlag, 1987.

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Частини книг з теми "Chaotic modes"

1

Mikhlin, Yuri V., Katarina Yu Plaksiy, Tatyana V. Shmatko, and Gayane V. Rudneva. "Normal Modes of Chaotic Vibrations and Transient Normal Modes in Nonlinear Systems." In Advanced Structured Materials, 85–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92234-8_6.

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2

Sato, Masayuki, Masato Sakai, and A. J. Sievers. "Driven Intrinsic Localized Modes in Soft Nonlinear Microscopic and Macroscopic Lattices." In 13th Chaotic Modeling and Simulation International Conference, 783–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70795-8_55.

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3

Shaw, Steven W., and Shang-Rou Hsieh. "The Local Stability of Inactive Modes in Chaotic Multi-Degree-of-Freedom Systems." In Bifurcation and Chaos: Analysis, Algorithms, Applications, 333–42. Basel: Birkhäuser Basel, 1991. http://dx.doi.org/10.1007/978-3-0348-7004-7_43.

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4

Aceves, A., D. D. Holm, and G. Kovacic. "Chaotic Dynamics Due to Competition Among Degenerate Modes in a Ring-Cavity Laser." In Springer Series in Nonlinear Dynamics, 218–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77769-1_40.

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5

Bakunin, Oleg G. "Fractional Models of Anomalous Transport." In Chaotic Flows, 181–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20350-3_11.

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Hanslmeier, Arnold. "Chaotic Dynamo Models." In The Chaotic Solar Cycle, 153–90. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9821-0_8.

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7

Wiggins, Stephen. "Markov Models." In Chaotic Transport in Dynamical Systems, 193–208. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-3896-4_5.

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Nurmi, Hannu. "Chaotic Behavior of Models." In Voting Procedures under Uncertainty, 13–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-540-24830-9_2.

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Zuchowski, Lena C. "Evaluation of Chaotic Models." In A Philosophical Analysis of Chaos Theory, 81–126. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54663-6_4.

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Shen, Bo-Wen, R. A. Pielke, X. Zeng, J. J. Baik, S. Faghih-Naini, J. Cui, R. Atlas, and T. A. L. Reyes. "Is Weather Chaotic? Coexisting Chaotic and Non-chaotic Attractors Within Lorenz Models." In 13th Chaotic Modeling and Simulation International Conference, 805–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70795-8_57.

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Тези доповідей конференцій з теми "Chaotic modes"

1

Shashikhin, V. N., L. G. Potapova, and S. V. Budnik. "Chaotic Modes Suppression in Nonlinear Systems." In 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2021. http://dx.doi.org/10.1109/icieam51226.2021.9446444.

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2

KUSMARTSEV, FEO V., and KARL E. KÜRTEN. "CHAOTIC MODES IN SCALE FREE OPINION NETWORKS." In Proceedings of the 32nd International Workshop. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814289153_0006.

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3

Cao, Hui W. "Lasing modes in wave-chaotic semiconductor microcavities." In Physics and Simulation of Optoelectronic Devices XXX, edited by Marek Osiński, Yasuhiko Arakawa, and Bernd Witzigmann. SPIE, 2022. http://dx.doi.org/10.1117/12.2615434.

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4

Yao, Weiguang, Pei Yu, and Chris Essex. "Estimation of Chaotic Parameter Regimes via Generalized Competitive Modes Approach." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/de-23224.

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Анотація:
Abstract A procedure is proposed to estimate the parameter regimes of chaos in nonlinear systems by implementing a mathematical version of mode competition. The idea is that for a system to be chaotic there must exist at least two generalized competitive modes in the system. The Lorenz system and a thin plate in flow-induced vibrations system are analyzed to find chaotic regimes by this procedure.
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5

Ohtomo, Takayuki. "Collective Chaos Synchronization among Modes in a Chaotic Three-Mode Laser." In EXPERIMENTAL CHAOS: 7th Experimental Chaos Conference. AIP, 2003. http://dx.doi.org/10.1063/1.1612203.

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Fang, Wei, Alexey Yamilov, and Hui Cao. "Study of High Quality Modes in Fully Chaotic Microcavities." In Frontiers in Optics. Washington, D.C.: OSA, 2004. http://dx.doi.org/10.1364/fio.2004.ftug7.

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7

Rysev, P. V., D. V. Rysev, V. K. Fedorov, K. S. Shulga, and S. Yu Pruss. "Identifying and modeling chaotic modes in electrical power systems." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239499.

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8

Kwon, Tae-yoon, Chil-min Kim, and Young-jai Park. "Passive Resonances and Active Lasing Modes in a Chaotic Microcavity." In 2006 International Conference on Transparent Optical Networks. IEEE, 2006. http://dx.doi.org/10.1109/icton.2006.248451.

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9

Perinova, Vlasta, and Antonin Luks. "Photon number and quantum phase properties of correlated chaotic modes." In 12th Czech-Slovak-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics, edited by Jan Perina, Sr., Miroslav Hrabovsky, and Jaromir Krepelka. SPIE, 2001. http://dx.doi.org/10.1117/12.417811.

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10

Rex, N. B., R. K. Chang, L. J. Guido, D. Bour, and M. Kneissl. "Directional laser emission from chaotic modes in quadrupole-deformed GaN microdisks." In Conference on Lasers and Electro-Optics (CLEO 2000). Technical Digest. Postconference Edition. TOPS Vol.39. IEEE, 2000. http://dx.doi.org/10.1109/cleo.2000.906881.

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Звіти організацій з теми "Chaotic modes"

1

Cai, Qin. Detecting Chaotic Signals with Nonlinear Models. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6448.

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2

Hunt, Brian R., Edward Ott, and James A. Yorke. Chaotic Models and Anomaly Detection for Complex Data Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada563464.

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3

Mukerji, S., J. M. McDonough, M. P. Menguec, S. Manickavasagam, and S. Chung. Chaotic map models of soot fluctuations in turbulent diffusion flames. Office of Scientific and Technical Information (OSTI), October 1998. http://dx.doi.org/10.2172/676978.

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4

Geller, Jil T., Sharon E. Borglin, and Boris A. Faybishenko. Experiments and evaluation of chaotic behavior of dripping waterin fracture models. Office of Scientific and Technical Information (OSTI), June 2001. http://dx.doi.org/10.2172/900684.

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5

Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, March 2019. http://dx.doi.org/10.34074/ocds.12019.

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Анотація:
Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
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6

Faybishenko, Boris, Christine Doughty, and Jil T. Geller. Chaotic-Dynamical Conceptual Model to Describe Fluid Flow and Contaminant Transport in a Fractured Vadose Zone. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/828253.

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7

Faybishenko, Boris, Christine Doughty, Thomas M. Stoops, thomas R. Wood, and Stephen W. Wheatcraft. A Chaotic-Dynamical Conceptual Model to Describe Fluid flow and Contaminant Transport in a Fractured Vadose zone. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/828337.

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8

Faybishenko, Boris, Yifeng Wang, Jon Harrington, Elena Tamayo-Mas, Jens Birkholzer, and Carlos Jové-Colón. Phenomenological Model of Nonlinear Dynamics and Deterministic Chaotic Gas Migration in Bentonite: Experimental Evidence and Diagnostic Parameters. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1856510.

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9

Ray, Laura, Madeleine Jordan, Steven Arcone, Lynn Kaluzienski, Benjamin Walker, Peter Ortquist Koons, James Lever, and Gordon Hamilton. Velocity field in the McMurdo shear zone from annual ground penetrating radar imaging and crevasse matching. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42623.

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Анотація:
The McMurdo shear zone (MSZ) is strip of heavily crevassed ice oriented in the south-north direction and moving northward. Previous airborne surveys revealed a chaotic crevasse structure superimposed on a set of expected crevasse orientations at 45 degrees to the south-north flow (due to shear stress mechanisms). The dynamics that produced this chaotic structure are poorly understood. Our purpose is to present our field methodology and provide field data that will enable validation of models of the MSZ evolution, and here, we present a method for deriving a local velocity field from ground penetrating radar (GPR) data towards that end. Maps of near-surface crevasses were derived from two annual GPR surveys of a 28 km² region of the MSZ using Eulerian sampling. Our robot-towed and GPS navigated GPR enabled a dense survey grid, with transects of the shear zone at 50 m spacing. Each survey comprised multiple crossings of long (> 1 km) crevasses that appear in echelon on the western and eastern boundaries of the shear zone, as well as two or more crossings of shorter crevasses in the more chaotic zone between the western and eastern boundaries. From these maps, we derived a local velocity field based on the year-to-year movement of the same crevasses. Our velocity field varies significantly from fields previously established using remote sensing and provides more detail than one concurrently derived from a 29-station GPS network. Rather than a simple velocity gradient expected for crevasses oriented approximately 45 degrees to flow direction, we find constant velocity contours oriented diagonally across the shear zone with a wavy fine structure. Although our survey is based on near-surface crevasses, similar crevassing found in marine ice at 160 m depth leads us to conclude that this surface velocity field may hold through the body of meteoric and marine ice. Our success with robot-towed GPR with GPS navigation suggests we may greatly increase our survey areas.
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Nissen, Mark E., and Omar A. Sawy. The Rolodex Model: Understanding Relationship Complexity as a Precursor to the Design of Organizational Forms for Chaotic Environments. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada407951.

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