• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.595-604
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• 2012

In this article, we consider chaotic behavior happened in nonsmooth dynamical systems. To quantify such a behavior, a computation of Lyapunov exponents for chaotic orbits of a given nonsmooth dynamical system is focused. The Lyapunov exponent is a very important concept in chaotic theory, because this quantity measures the sensitive dependence on initial conditions in dynamical systems. Therefore, Lyapunov exponents can decide whether an orbit is chaos or not. To measure the sensitive dependence on initial conditions for nonsmooth dynamical systems, the calculation of Lyapunov exponent plays a key role, but in a theoretical point of view or based on the definition of Lyapunov exponents, Lyapunov exponents of nonsmooth orbit could not be calculated easily, because the Jacobian derivative at some point in the orbit may not exists. We use an algorithmic calculation method for computing Lyapunov exponents using time series for a two dimensional piecewise smooth dynamic system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.43.1-43
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• 2001

We propose two control methods of the Lyapunov exponents for Jordan-type recurrent neural networks. Both the two methods are formulated by a gradient-based learning method. The first method is derived strictly from the definition of the Lyapunov exponents that are represented by the state transition of the recurrent networks. The first method can control the complete set of the exponents, called the Lyapunov spectrum, however, it is computationally expensive because of its inherent recursive way to calculate the changes of the network parameters. Also this recursive calculation causes an unstable control when, at least, one of the exponents is positive, such as the largest Lyapunov exponent in the recurrent networks with chaotic dynamics. To improve stability in the chaotic situation, we propose a non recursive formulation by approximating ...

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1844-1847
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• 1997

Assuming that EEG(electroencephalogram), which is generated by a nonlinear electrical of billions of neurons in the brain, has chaotic characteristics, it is confirmend by frequency spectrum analysis, log frequency spectrum analysis, correlation dimension analysis and Lyapunov exponents analysis. Some chaotic characteristics are related to the degree of brain activity. The slope of log frequency spectrum increases and the correlation dimension decreasess with respect to the activities, while the largest Lyapunov exponent has only a rough correlation.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.434-447
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• 2016

The aim of this paper is the study of instability state of boiling water reactors with a method based in largest Lyapunov exponents (LLEs). Detecting the presence of chaos in a dynamical system is an important problem that is solved by measuring the LLE. Lyapunov exponents quantify the exponential divergence of initially close state-space trajectories and estimate the amount of chaos in a system. This method was applied to a set of signals from several nuclear power plant (NPP) reactors under commercial operating conditions that experienced instabilities events, apparently each of a different nature. Laguna Verde and Forsmark NPPs with in-phase instabilities, and Cofrentes NPP with out-of-phases instability. This study presents the results of intrinsic instability in the boiling water reactors of three NPPs. In the analyzed cases the limit cycle was not reached, which implies that the point of equilibrium exerts influence and attraction on system evolution.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.12
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• pp.1615-1623
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• 1997

Numerical study on the chaotic stirring of the screw extruder model proposed has been performed. The velocity field was used in obtaining the trajectories of passive particles for studying the stirring effect of the screw extruder. Two nonlinear dynamical tools, that are Poincare sections and Lyapunov exponents, were used in analysing the stirring effect. The Poincare sections and the Lyapunov exponents show that the stirring effect is most satisfactory, when n(the number of flights in a section) is 1, for the case a (aspect ratio ; flight height divided by the spacing between flights) being O.1. It is also required to set n=3, or 5 at a= 0.2, 0.3 for a uniform stirring.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.478-485
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• 1999

The purpose of this paper was the determination of the relationship between the chaotic charateristics and various levels of brain activities. Assuming that EEG(eletroencephalogram), which is generated by a nonlinear electiecal behavior of billions of neurons in the brain, has chaotic characteristics, it was confirmed by frequency spectrum analysis, log frequency spectrum analysis, correlation dimension analysis and Lyapunov exponents analysis. Chaotic characteristics are related to the degree of brain activity. The slope of log frequency spectrum increased and the correlation dimension decreased with respect to the brain activities, while the lagrest Lyapunov exponent has some rough correlation.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.3
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• pp.169-176
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• 2000

The partial discharge(PD) signal, may seems to be stochastic and merely random, was investigated using the method to discern between chaos and random signal, e.g. correlation integral, Lyapunov characteristic exponents and etc. For the purpose of obtaining experimental data, partial discharge detecting system via computer aided acoustic sensor, detect PD signal from the insulating system, was used. While this method is very different from typical statistical analysis from the point of view of a nonlinear analysis, it can provide better interpretable criterion according to the time evolution with a degradation process in the same type insulating system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.165-168
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• 1999

The chaotic characteristics of partial discharge(PD), may seems to be stochastic and merely random, were investigated using the method to discern between chaos and random signal, e.g. correlation integral, Lyapunov characteristic exponents and etc. For the purpose of obtaining experimental data, computer aided partial discharge detecting system was used. While this method is very different from typical statistical analysis from the point of view of a nonlinear analysis, it can provide better interpretable criterion according to the time evolution with a degradation process in the same type insulating system.

• The Journal of Natural Sciences
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• v.6 no.1
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• pp.79-87
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• 1993

In the map of period-doubling bifurcation, stable fixed points bifurcate to $2^n$ fixed points, and in the chaotic region, the chaotic bands merge to 1/$2^n$ bands. In a typical map, the chaotic bands expand to a broad chaotic band during the merging process, so called crisis. In this paper, interior crises appearing during the merging process will be discussed by using our map and will be analyzed the characteristics of the phenomenon by obtaining the Lyapunov Exponents.

• Journal of Welding and Joining
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• v.16 no.6
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• pp.86-96
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• 1998

This study proposes the analysis method of time series ultrasonic signal using the chaotic feature extraction for degradation extent evaluation. Features extracted from time series data using the chaotic time series signal analyze quantitatively degradation extent. For this purpose, analysis objective in this study is fractal dimension, lyapunov exponent, strange attractor on hyperspace. The lyapunov exponent is a measure of the rate at which nearby trajectories in phase space diverge. Chaotic trajectories have at least one positive lyapunov exponent. The fractal dimension appears as a metric space such as the phase space trajectory of a dynamical system. In experiment, fractal correlation) dimensions, lyapunov exponents, energy variation showed values of 2.217∼2.411, 0.097∼ 0.146, 1.601∼1.476 voltage according to degardation extent. The proposed chaotic feature extraction in this study can enhances precision ate of degradation extent evaluation from degradation extent results of the degraded materials (SA508 CL.3)