• Communications of the Korean Mathematical Society
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• v.18 no.1
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• pp.105-115
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• 2003

It is well known that there exists a regular branched covering map from T$^2$ onto $\={C}$ iff the ramification indices are (2,2,2,2), (2,4,4), (2,3,6) and (3,3,3). In this paper we construct (count-ably many) chaotic homeomorphisms induced by hyperbolic toral automorphism and regular branched covering map corresponding to the ramification indices (2,2,2,2). And we also gave an example which shows that the above construction of a chaotic map is not true in general if the ramification indices is (2,4,4) and also show that there are no chaotic homeomorphisms induced by hyperbolic toral automorphism and regular branched covering map corresponding to the ramification indices (2,3,6) and (3,3,3).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.811-815
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• 2005

We theoretically studied synchronization of chaotic oscillation in semiconductor lasers with chaotic light injection feed-back induced chaotic light generated from a master semiconductor laser was injected into a solitary slave semiconductor laser. The slave laser subsequently exhibited synchronized chaotic output for a wide parameter range with strong injection and frequency detuning within the injection locking scheme. We also analytically examined chaos synchronization based on a linear stability analysis from the view point of synchronization based on a linear stability analysis from the view point of modulation response of injection locked semiconductor lasers to chaotic light signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.729-736
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• 2003

In this paper, we propose that the chaotic behavior analysis in the mobile robot of embedding some chaotic such as Chua`s equation, Arnold equation with obstacle. In order to analysis of chaotic behavior in the mobile robot, we apply not only qualitative analysis such as time-series, embedding phase plane, but also quantitative analysis such as Lyapunov exponent In the mobile robot with obstacle. We consider that there are two type of obstacle, one is fixed obstacle and the other is VDP obstacle which have an unstable limit cycle. In the VDP obstacles case, we only assume that all obstacles in the chaos trajectory surface in which robot workspace has an unstable limit cycle with Van der Pol equation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1410-1417
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• 2004

In this paper, we propose that the chaotic behavior analysis in the chaotic mobile robot embedding Arnold, equation, Chua's equation and hyper-chaos equation. In order to analysis of chaotic behavior in the mobile robot, we apply not only qualitative analysis such as time-series, embedding phase plane, but also quantitative analysis such as Lyapunov exponent in the mobile robot with obstacle.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1021-1024
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• 2000

In this paper, a neuro-fuzzy system for quantitative characterization of chaotic signals is proposed. The proposed system is differ from the previous methods in that the nonlinear functions of the nonlinear dynamical systems are calculated as the invariant factor. In the proposed neuro-fuzzy system, the nonlinear functions are determined by supervised learning. From the reconstructed nonlinear functions, the proposed system can generate extrapolated chaotic signals. This feature will help the study of nonlinear dynamical systems which require large number of chaotic data. To confirm the validity of the proposed system, nonlinear functions are reconstructed from 1-dimensional and 2-dimensional chaotic signals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.118-124
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• 2004

In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1083-1085
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• 1996

Controlling chaos is a new concept, which transform chaotic signal to fixed points, or low periodic orbits. In this paper we propose state feedback method in order to control chaotic signal in canonical Chua's circuit Canonical Chua's circuit is a simple electronic circuit consists of two linear resistors, a linear inductor, two linear capacitors, and only one nonlinear element so called Chua's diode. This nonlinear element supplies power to the circuit and drives the chaotic oscillations. Proposed control method is successful to control chaotic signal in canonical Chua's circuit Result shows that chaotic trajectory change rapidly its orbit to stable fixed points, 1 periodic orbit, or 2 periodic orbit when control signal applies.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.407-410
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• 1996

In this Paper, we propose the feedback method having neural network to control the chaotic signals to periodic signals. This controller has very simple structure, it is immune to small parameter variations, the precise access to system parameters is not required and it is possible to follow ones of its inherent periodic orbits or the desired orbits without error, The controller consist of linear feedback gain and neural network. The learning of neural network is achieved by error-backpropagation algorithm. To prove and analyze the proposed method, we construct a software tool using c-language.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.41-48
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• 2003

This paper presents an indirect adaptive neuro controller using modified chaotic neural networks(MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new Dynamic Backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and the indirect adaptive neuro controller. The simulation results show good performances, since the MCNN has robust adaptability to nonlinear dynamic system.