• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.282-282
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• 2000

The analysis of the radiation effect on matter has been performed using stochastic methods. Recently, It was discovered that the detector pulses of radiation can be analysed using deterministic method that utilizes the chaotic behaviour with an attractor found in a noise region. We acquired a time series for pulse tram of Am-241 using scintillation detector and reconstructed a phase space, then performed new analysis for the radiation detection signal by applying embedding theory, Lyapunov exponent, correlation dimension, autocorrelation dimension, and power spectrum.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.284-284
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• 2000

In adaptive fuzzy control systems. fuzzy systems are used to approximate the unknown plant nonlinearities. Until now. most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure based on system state availability. This paper considers observer-based nonlinear controller and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters for state observer and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.987-993
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• 1996

This report suggests a new non-linear friction compensation for digital control systems. This control adopts a hysteric nonlinear clement which can introduce the phase lead of the control system to compensate the phase delay comes from the inherent time delay of a digital control. The Lyapunov direct method is used to prove the asymtotic stability of the suggested control, and the stability and the effectiveness are verified analytically and experimentally on a single axis servo driving system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.135-139
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• 2000

This research proposes a nonlinear sliding mode control. The sliding mode control is designed according to Lyapunov function. The equivalent control term is estimated by neural network. To estimate the unknown part in the control law in on-line fashion, A recurrent neural network is given as on-line estimator. The stability of the control system is guaranteed owing to the on-line learning ability of the recurrent neural network. It is certificated through simulation results to be applied to nonlinear system that the function approximation and the proposed control scheme is very effective.

• International Journal of Aeronautical and Space Sciences
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• v.5 no.1
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• pp.19-29
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• 2004

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.16 no.2
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• pp.85-91
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• 2012

In this paper, we study uniform exponential stabilization of the vibrations of the Kelvin-Voigt type wave equation with acoustic boundary in a bounded domain in $R^n$. To stabilize the systems, we incorporate separately, the internal material damping in the model as like Gannesh C. Gorain [1]. Energy decay rates are obtained by the exponential stability of solutions by using multiplier technique.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.895-897
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• 1988

This paper is concerned with linear system containing time delay in model reference adaptive control. An algorithm is developed using Lyapunov second method and properties of strictly positive real function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.294-297
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• 1996

This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.207-215
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• 2013

In this paper, we implement an empirical study to test whether the time series of daily returns in stock and Won/USD exchange markets is strictly stationary or explosive. The results indicate that only a few series show nonstationary volatility when dramatic events erupted; in addition, this nonstationary behavior occurs more often in the Won/USD exchange market than in the stock market.