• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1958-1960
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• 2001

In this paper, we propose the design methodology of target tracking system using the identification of TS fuzzy model based on genetic algorithm(GA) and RLS algorithm. In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. In this paper, to resolve these problems of nonlinear filtering technique, the error of EKF by nonlinearity is compensated by identifying TS fuzzy model. In the proposed method, after composing training datum from the parameters of EKF, by identifying the premise and consequent parameters and the rule numbers of TS fuzzy model using GA, and by tuning finely the consequent parameters of TS fuzzy model using recursive least square(RLS) algorithm, the error of EKF is compensated. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1854-1855
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• 2006

In this paper, the Takagi-Sugeno (TS) fuzzy state estimation scheme, which is suggested for a steady state estimator using standard Kalman filter theory with uncertainties. In that case, the steady state with uncertain can be represented by the TS fuzzy model structure, which is further rearranged to give a set of uncertain linear model using standard Kalman filter theory. And then the unknown uncertainty is regarded as an additive process noise. To optimize fuzzy system, we utilize the genetic algorithm. The steady state solutions can be found for proposed linear model then the linear combination is used to derive a global model. The proposed state estimator is demonstrated on a truck-trailer.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.17-20
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• 2002

This paper proposes a new 75 fuzzy model approximation method which reduces error in nonlinear fuzzy model approximation over the V-type decision rules. Employing rational Bezier curves used in computer graphics to represent curves or surfaces, the proposed method approximates the decision rule by constructing a tractable linear equation in the highly non-linear fuzzy rule interval. This algorithm is applied to the self-adjusting air cushion for spinal cord injury patients to automatically distribute the patient's weight evenly and balanced to prevent decubitus. The simulation results indicate that the performance of the proposed method is bettor than that of the conventional TS Fuzzy model in terms of error and stability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.449-452
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• 1997

In this paper, we suggest a variable structure controller using the time-varying nonlinear sliding surface instead of the fixed sliding surface, which has been the robustness against parameter variations and extraneous disturbance during the reaching phase. As appling TS fuzzy algorithm to the regulation of the nonlinear sliding surface, the reaching time of the system trajectory is faster than the fixed method . This proposed scheme has better performance than the conventional method in reaching time parameter variation and extraneous disturbance. To demonstrate its performance, the proposed control algorithm is applied to a rotational inverted pendulum.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.17-28
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• 2002

Recently, the study on non-linear equalization, self-recovering equalization using the neural Network structure or Fuzzy logic, is lively in progress. In this thesis, if the value of error difference is large, coefficient adaptation rate is bigger, and if being small, it is smaller. We proposed the new FSG(Fuzzy Stochastic Gradient)/CMA algorithm combining TS(Tagaki-Sugeno) fuzzy model having fast convergence rate and low mean square error(MSE) and CMA(Constant Modulus Algorithm) which is prone to ISI and insensitive to phase alteration. As a simulation result of the designed channel adaptive equalizer using the proposed FSG/CMA algorithm, it is shown that SNR is improved about 3.5dB comparing to the conventional algorithm. 

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.395-399
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• 2001

This paper describes modular fuzzy inference systems(MFIS) with adaptive capability to extract fuzzy inference modules from observation data through the learning process. The proposed MFIS is based on the structural similarity to Tagaki-Sugeno fuzzy models and a modular neural architecture. The learning of MFIS is done by assigning new fuzzy inference modules and by updating the parameters of existing modules. The fuzzy inference modules consist of local model network and fuzzy gating network. The parameters of the MFIS are updated by the standard LMS algorithm. The performance of the MFIS is illustrated with adaptive control of a nonlinear dynamic system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.43-48
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• 2010

This article presents the experimental studies of controlling angle and position of the inverted pendulum system using neural network to compensate for errors caused due to fuzzy controller. Although fuzzy control method can deal with nonlinearities of the system, fixed fuzzy rules may not work and result in tracking errors in some cases. First, a nominal Takagi-Sugeno (TS) type fuzzy controller with fixed weights is used for controlling the inverted pendulum system. Then the neural network is added at the reference input to form the reference compensation technique (RCT)control structure. Neural network modifies the input trajectories to improve system performances by updating internal weights in on-line fashion. The back-propagation learning algorithm for neural network is derived and used to update weights. Control hardware of a DSP 6713 board to have real time control is implemented. Experimental results of controlling inverted pendulum system are conducted and performances are compared.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.457-463
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• 2001

This paper presents a simple and new digital redesign algorithm for fuzzy-model-based controllers. In the first stage, a continuous-time TS fuzzy model is constructed for a given continuous-time nonlinear system and a corresponding continuous-time fuzzy-model-based controller is established based on the existing controller synthesis algorithms. In the second stage, the continuous-time fuzzy-model-based controller is converted to equivalent discrete-time fuzzy-model-based controller, aiming at maintaining the property of the analogue controlled system, which are called intelligent digital redesign. Finally, the proposed method is applied to the digital control of inverted pendulum system to shows the effectiveness and the effectiveness and the feasibility of the method.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.2-35
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• 2002

$\textbullet$ Introduction $\textbullet$ An affine fuzzy system $\textbullet$ The stabilization of an affine fuzzy system $\textbullet$ Iterative LMI algorithm for the stabilization $\textbullet$ A numerical example $\textbullet$ Conclusion