• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.502-505
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• 2004

An intelligent digital redesign technique (IDR) for the observer-based output feedback Takagi-Sugeno (T-S) fuzzy control system with unmeasurable premise variables is developed. The considered IDR condition is cubically parameterized as convex minimization problems of the norm distances between linear operators to be matched.

• Communications of the Korean Mathematical Society
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• v.24 no.1
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• pp.17-27
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• 2009

In the present work, we introduce two types of compatible maps and prove a common fixed point theorem for such maps in Menger probabilistic metric spaces. Our result generalizes and extends many known results in metric spaces and fuzzy metric spaces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.31-34
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• 1998

본 논문에서는 하이브리드 구조를 가지는 자기구성 퍼지제어기를 제안한다. 제안한 방법은 FARMA 제어기에 비해 다음과 같은 장점을 가진다. 하이브리드 구조를 자기구성 퍼지논리 제어기에 도입하므로써 예측출력값을 구할 때 까지의 입축력정보의 부재로 인한 나쁜 응답성능을 개선할 수 있다. 또한 이 방법은 Yager의 t-norm을 이용하여 계산상의 복잡성을 피하고 규칙들의 가중치를 구하기 위해 필요한 Dmax선정의 어려움을 해결한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.151-157
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• 2008

A non-fragile guaranteed cost control (GCC) problem is presented for a class of discrete time-delay nonlinear systems described by Takagi-Sugeno (T-S) fuzzy model. The systems are assumed to have norm-bounded time-varying uncertainties in the matrices of state, delayed state and control gains. Sufficient conditions are first obtained which guarantee that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound. Then the design method of the non-fragile guaranteed cost controller is formulated in terms of the linear matrix inequality (LMI) approach. A numerical example is given to illustrate the effectiveness of the proposed design method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.859-861
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• 1996

This paper presents the novel fault diagnostic expert system based on dissolved gas analysis(DGA) techniques in power transformer. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set concept. The uncertainty of rules are handled by fuzzy measures. Trend analysis through the monthly increment of key gas and DGA analysis are combined by the Dempster-Shafer theory, and the state of transformer and confidence factor are yielded by using this combined analysis. To verify the effectiveness of the proposed diagnosis technique, the expert system has been tested by using KEPCO's transformer gas records.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.143-145
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• 1996

This paper presents the hybrid type based expert system for fault diagnosis in transformers. The proposed system uses the novel fault diagnostic technique based on dissolved gas analysis(DGA) in oil-immersed transformers. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set. Also, the uncertainty of the fault diagnostic rules are handled by using fuzzy measures. Finally, kohnen's feature map performs fault classification in transformers. To verify the effectiveness of the proposed diagnosis technique, the hybrid type based expert system for fault diagnosis has been tested by using KEPCO's transformer gas records.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.95-98
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete -time system have proper reason. Sufficiently conditions for the global state -matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMls). Finally, we prove the effectiveness and stabilization of the proposed intelligent digital redesign method by applying the chaotic Lorentz system.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.185-190
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• 2005

A systematic output tracking control design technique for robust control of Takagi-Sugeno (T-S) fuzzy systems with norm bounded uncertainties is developed. The uncertain T-S fuzzy system is first represented as a set of uncertain local linear systems. The tracking problem is then converted into the stabilization problem for a set of uncertain local linear systems thereby leading to a more feasible controller design procedure. A sufficient condition for robust asymptotic output tracking is derived in terms of a set of linear matrix inequalities. A stability condition on the traversing time instances is also established. The output tracking control simulation for a flexible-joint robot-arm model is demonstrated, to convincingly show the effectiveness of the proposed system modeling and controller design.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.103-115
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• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.