• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1361-1369
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• 2000

For the implementation of 3D Sound Localization system, the binaural filtering by HRTFs is generally employed. But the HRTF filter is of high order and its coefficients for all directions have to be stored, which imposes a rather large memory requirement. To cope with this, research works have centered on obtaining low dimensional HRTF representations without significant loss of information and synthesizing the original HRTF efficiently, by means of feature extraction methods for multivariate dat including PCA. In these researches, conventional linear PCA was applied to the frequency domain HRTF data and using relatively small number of principal components the original HRTFs could be synthesized in approximation. In this paper we applied neural network based nonlinear PCA model (NLPCA) and the nonlinear PLS repression model (NLPLS) for this low dimensional HRTF modeling and analyze the results in comparison with the PCA. The NLPCA that performs projection of data onto the nonlinear surfaces showed the capability of more efficient HRTF feature extraction than linear PCA and the NLPLS regression model that incorporates the direction information in feature extraction yielded more stable results in synthesizing general HRTFs not included in the model training.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5194-5201
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• 2011

In this paper, an adaptive nonlinear friction compensation scheme for second-order nonlinear mechanical system with a partially known nonlinear dynamic friction is proposed to achieve asymptotic position and velocity tracking in the absence of disturbances and modeling errors. It is also shown that even with disturbances and modeling errors, in contrast to existing other adaptive control schemes, by proper adjustment of design parameters, reduced error bounds on position and velocity tracking can be achieved.

• Journal of IKEEE
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• v.13 no.1
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• pp.30-35
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• 2009

A new controller which can achieve the asymptotic tracking is proposed for the nonlinear system having a uncertainty in the input coefficient. A high gain observer is used to estimate the state variables when the nonlinear system has a modeling uncertainty. A variable structure control is used to achieve an asymptotic tracking, while ultimate boundness was achieved in the previous work. A Lyapunov analysis is used to justify the our proposal. The performance of proposed method is demonstrated via simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.646-651
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• 2004

This paper deals with a new adaptive fuzzy sliding mode controller and its application to an inverted pendulum. We propose new method of adaptive fuzzy sliding mode control scheme that the fuzzy logic system is used to approximate the unknown system functions in designing the SMC of uncertain nonlinear systems. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.50-59
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• 1996

In this paper, an adaptive precompensator, which can reduce the distortion of a Wiener system effectively, is proposed. The previous techniques for adaptive precompensation, based on the Volterra series modeling to compensate the distortion of a nonlinear system, are not suitable for real-time implementation due to high computational burden and slow convergence burden and slow convergence rate. This paper presents an adaptive precompensation technique for the class of nonlinear subsystem, referred to as Wiener system. An adaptive algorithm for adjusting the parameters of a precompensator, structured by a hammerstein model, is derived using the stochastic gradient method. Also, an adaptive precompensatin technique which can effectively reduce nonlinear distortion in μ-law type of saturation characteristics is proposed. The validity of the proposed algorithm is confirmed through simulation by applying it to known Wiener systmes and a typical loudspeaker model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.12-18
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• 2011

This paper deals with a new adaptive fuzzy sliding mode controller and its application to an inverted pendulum. We propose a new method of adaptive fuzzy sliding mode control scheme that the fuzzy logic system is used to approximate the unknown system functions in designing the SMC of uncertain nonlinear systems. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.2
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• pp.27-36
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• 2007

This study, the present study wishes to develop a model that estimates shear strength characteristics of steel fiber reinforced concrete using artifical neural network models. Neural network models, developed as mathematical models, are being widely used not only in its original purpose of pattern recognition, but also in application fields by the function's nonlinear loaming and interpolar ability Neural network has a repetitive rotation algorithm that can cyclically and repeatedly estimate system conditions and parameter ideal values, and it can be used in the modeling of the nonlinear system by nonlinear characteristic functions that construct the system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.267-270
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• 1997

In this paper, we proposed an optimazation method using Genetic Algorithm for nonlinear system modeling. Fuzzy Neural Network(FNNs) was used as basic model of nonlinear system. FNNs was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, We used FNNs which was proposed by Yamakawa. The FNNs was composed Simple Inference and Error Back Propagation Algorithm. To obtain optimal model, parameter of membership function, learning rate and momentum coefficient of FNNs are tuned using genetic algorithm. And we used simplex algorithm additionaly to overcome limit of genetic algorithm. For the purpose of evaluation of proposed method, we applied proposed method to traffic choice process and waste water treatment process, and then obtained more precise model than other previous optimization methods and objective model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.696-705
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• 1997

This paper presents an efficient adaptive predistortion technique compensating linear and nonlinear distortions caused by high-power amplifier (HPA) with memory in OFDM systems. The efficient adaptive data predistortion techniques proposed for compensation of HPA with memory in single carrier systems cannot be applied to OFDM systems since the possible input levels for HPA is infinite in OFDM systems. Also, previous adaptive predistortion techniques, based on Volterra series modeling, are not suitable for real-time implementation due to high computational burden and slow convergence rate. In the proposed approach, the memoryless HPA preceded by a linear filter in OFDM systems is modeled by the Wiener system which is then precompensated by the proposed adaptive predistorter with a minimum number of filter taps. An adaptive algorithm for adjusting the proposed adaptive predistorter is derived using the stochastic gradient method. It is demonstrated by computer simulation that the performance of OFDM system suffering from nonlinear distortion can be greatly improved by the proposed efficient adaptive predistorter using a small number of filter taps.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.69-79
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• 2010

This paper describes the experimental framework for the control system design and validation of a rotorcraft unmanned aerial vehicle (UAV). Our approach follows the general procedure of nonlinear modeling, linear controller design, nonlinear simulation and flight test but uses an indoor-installed multi-camera system, which can provide full 6-degree of freedom (DOF) navigation information with high accuracy, to overcome the limitation of an outdoor flight experiment. In addition, a 3-DOF flying mill is used for the performance validation of the attitude control, which considers the characteristics of the multi-rotor type rotorcraft UAV. Our framework is applied to the design and mathematical modeling of the control system for a quad-rotor UAV, which was selected as the test-bed vehicle, and the controller design using the classical proportional-integral-derivative control method is explained. The experimental results showed that the proposed approach can be viewed as a successful tool in developing the controller of new rotorcraft UAVs with reduced cost and time.