• The Journal of Natural Sciences
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• v.7
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• pp.5-9
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• 1995

We have controlled the chaotic output of Lorenz equation by using a return map. By applying small perturbations, the chaotic temporal behaviors and phase diagrams have been stabilized to period-1T, -2T, -3T which are unstable fixed points respectively as the same behaviors as the OGY method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05a
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• pp.100-105
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• 2004

In this paper, we propose a method to avoid obstacles that have unstable limit cycles in a chaos trajectory surface. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. When a chaos robot meets an obstacle in a Lorenz equation or Hamilton equation trajectory, the obstacle reflects the robot. We also show computer simulation results for avoidance obstacle which fixed obstacles and hidden obstacles of Lorenz equation and Hamilton equation chaos trajectories with one or more Van der Pol obstacles

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.2
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• pp.90-95
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• 2001

Electric arc furnaces (EAFs) has a process to cause the degradation of the electric power quality such as voltage flicker. In order to adequately understand and analyze the effects on the power system from these loads, obtaining an accurate representation of the characteristics of the loads is crucial. In this paper, a mixed chaotic EAF model to represent the low frequency and high frequency variations of the arc current respectively has been proposed. The Lorenz system may contribute to the low frequency components of arc current and the logistic equation may contribute to the high frequency components, and the proposed mixed model will be a combination of both Lorenz and logistic model. The concept of chaotic parameters, such as chaotic resistance, inductance of admittance has been also proposed for the characterization of arc furnace operation and the highly nonlinear physical processes. The power quality indices are calculated from the simulated waveforms and compared with the actual power quality indices statistics in order to illustrate the model's capabilities.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.453-456
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1692-1699
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• 2004

In this paper, we propose a method to avoid obstacles that have unstable limit cycles in a chaos trajectory surface. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. We also show computer simulation results of Arnold equation, Chua's equation, Hyper-chaos equation, Hamilton equation and Lorenz chaos trajectories with one or more Van der Pol obstacles.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.197-204
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• 2005

In this paper, we propose a method to an obstacle avoidance of chaotic robots that have unstable limit cycles in a chaos trajectory surface in the ubiquitous environment. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. We also show computer simulation results of Chua's equation, Lorenz equation, Hamilton and Hyper-chaos equation trajectories with one or more Van der Pol as an obstacles. We proposed and verified the results of the method to make the embedding chaotic mobile robot to avoid with the chaotic trajectory in any plane.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.142-146
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• 1994

This paper proposes an fill-factor algorithm that determines embedding parameters which are needed in optimal attractor reconstruction. For reliability test, using this algorithm, we reconstructs the attractor of numerical chaotic data such as Duffing equation, Lorenz equation and Rossler equation whose embedding parameters are known. Also we reconstructs the attractor of experimental data and evaluates correlation dimension. Experimental data used in this paper are 38 ECG data of AHA(American Heart Association) ECG database. For numerical chaotic data, correlation dimension and Lyapunov exponent of reconstructed attractor are very close to those of attractor using original coordinate system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.51-65
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• 2002

This paper presents a design method of the neural network-based controller using an indirect adaptive control method to deal with an intelligent control for chaotic nonlinear systems. The proposed control method includes the identification and control Process for chaotic nonlinear systems. The identification process for chaotic nonlinear systems is an off-line process which utilizes the serial-parallel structure of multilayer neural networks and simple state space neural networks. The control process is an on-line process which uses the trained neural networks as the system model. An error back-propagation method was used for training of identification and control for chaotic nonlinear systems. The performance of the proposed neural network controller was evaluated by application to the Duffing equation and the Lorenz equation, and the proposed controller was compared with other neural network-based controllers by computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2299-2301
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• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.