• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2215-2217
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• 2004

In this paper, a systematic design approach based on the parallel distributed compensation technique is proposed for chaotifying a general continuous-time Takagi-Sugeno (TS) fuzzy system. The fuzzy parallel distributed compensation controller (FPDCC) is composed of the feedback gain and time-delay feedback. The verification of chaos in the controlled continuous-time TS fuzzy system is done by the following procedures. First, we establish an asymptotically approximate relationship between a time-delay continuous-time TS fuzzy system and a discrete-time TS fuzzy system. Then, Marotto theorem is applied. Therefore, the generated chaos is in the sense of Li and Yorke. The boundedness in the controlled continuous-time TS fuzzy system is also proven via its associated discrete-time TS fuzzy system.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.193-197
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• 2002

This paper suggests a method to design TSK(Takagi-Sugeno-Kang) fuzzy nonlinear control system for automatic steering system which contains the nonlinear component of ship's maneuvering equation. A TSk fuzzy model can be identified using input-output data and represent a nonlinear system very well. A TSK fuzzy controller can be designed systematically from a TSK fuzzy model because the consequent part of TSK fuzzy rule is a linear input-output equation having a constant term. Therefore, this paper suggests the method identifying the TSK fuzzy model and designing the TSK fuzzy controller based on the TSK fuzzy model for ship steering.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.14-21
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• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.63-74
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• 2002

This paper presents an application result of on-line fault diagnosis system for glass melting furnace using a fuzzy expert system. Operators maintain the furnace using the furnace Knowledge and experience, which directly influence the furnace and glass product. Firstly, knowledge and experience is achieved and analyzed to implement the furnace Knowledge and experience into fuzzy expert system. The acquired Knowledges determined as a crisp rule or a fuzzy rule to expect its characteristics. And, a linear regression is used as the input of fuzzy rule to consider the exact knowledge of human operator. The fuzzy expert system is implemented with G2 which is an on-line expert system tool of Gensym Co. The application to a production furnace of Samsung-Corning Co. in Suwon shows successful results of proposed fuzzy expert system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1508-1512
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• 2012

This paper presents a relaxed stability condition for continuous-time affine fuzzy system using fuzzy Lyapunov function. In the previous studies, stability conditions for the affine fuzzy system based on quadratic Lyapunov function have a conservativeness. The stability condition is considered by using the fuzzy Lyapunov function, which has membership functions in the traditional Lyapunov function. Based on Lyapunov-stability theory, the stability condition for affine fuzzy system is derived and represented to linear matrix inequalities(LMIs). And slack matrix is added to stability condition for the relaxed stability condition. Finally, simulation example is given to illustrate the merits of the proposed method.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.1
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• pp.37-42
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• 2003

In this paper a self-organizing fuzzy system with rule pruning is proposed. A conventional self-organizing fuzzy system having only rule generation has a drawback in generating many slightly different rules from the existing rules which results in increased computation time and slowly learning. The proposed self-organizing fuzzy system generates fuzzy rules based on input-output data and prunes redundant rules which are caused by parameter training. The proposed system has a simple structure but performs almost equivalent function to the conventional self-organizing fuzzy system. Also, this system has better learning speed than the conventional system. Simulation results on several numerical examples demonstrate the performance of the proposed system.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.457-466
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• 2005

In this paper we present the concepts of fuzzy submachine, which are the generalized form of crisp submachine of a fuzzy finite state machine. Also we extend the concepts of system of generators to fuzzified form.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.52-57
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• 2003

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland can evaluate the usefulness of rules represented by classifiers with repeated learning. FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. It is that the antecedent and consequent of classifier is same as a fuzzy rule. In this paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented into fuzzy set. Then a pixel is decided whether it is edge pixel or not using fuzzy if-then rules. Depending on the average of gray levels, a number of fuzzy rules can be activated, and each rules makes the output. These outputs are aggregated and defuzzified to take new gray value of the pixel. To evaluate this edge detection, we will compare the new gray level of a pixel with gray level obtained by the other edge detection method such as Sobel edge detection. This comparison provides a reinforcement signal for FCS which is reinforcement learning. Also the FCS employs the Genetic Algorithms to make new rules and modify rules when performance of the system needs to be improved.

• Journal of the Korean Mathematical Society
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• v.44 no.6
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• pp.1383-1396
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• 2007

The aim of this paper is to study L-fuzzy uniformizable spaces. A new kind of topological fuzzy remote neighborhood system is defined and used for investigating the relationship between L-fuzzy co-topology and L-fuzzy (quasi-)uniformity. It is showed that this fuzzy remote neighborhood system is different from that in [23] when $\mathcal{U}$ is an L-fuzzy quasi-uniformity and they will be coincident when $\mathcal{U}$ is an L-fuzzy uniformity. It is also showed that each L-fuzzy co-topological space is L-fuzzy quasi-uniformizable.