• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2308-2310
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• 2000

In adaptive fuzzy control, 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. This paper considers general nonlinear systems and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters and fuzzy rule structrue are established so that the whole system is stable in the sense of Lyapunov.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1054-1057
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• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.625-631
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• 2000

In designing fuzzy model, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious-and-error process. This paper presents an approach to automatic design of optimal fuzzy rule bases for modeling using evolutionary programming. Evolutionary programming evolves simultaneously the structure and the parameter of fuzzy rule base a given task. To check the effectiveness of the suggested approach, 3 examples for modeling are examined, and the performance of the identified models are demonstrated.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.155-162
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance, FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and to increase efficiency. a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy PI and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and the resultant rule base is Macvicar-Whelan type. And the membership function is a Gaussian function. The frequency response information is used in tuning of the membership functions. Also a tuning strategy for the scaling factors is proposed based on the relationship between PID gain and the scaling factors. Simulation results show better performance and the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.261-264
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• 1992

In designing the fuzzy rule-based system, it has effected by the four significant factors such as the choice of membership function, scaling factor, the numbers of fuzzy control rule, the method of defuzzification. In this paper we design the fuzzy rule based system and evaluate by three factors, as followes reaching time, overshoot, and amplitude. And then we wiII show that the significant factors are the choice of scaling factor and the numbers of fuzzy control rule, and the system performance can be improved by the proper selection of the scaling factors.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.179-190
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• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.358-363
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• 1998

An algorithm for representing the cubic spline interpolation of differentiable functions by a fuzzy system is presented in this paper. The cubic B-spline functions which form a basis for the interpolation function are used as the fuzzy sets for input fuzzification. The ordinal number of the coefficient cKL in the list of the coefficient cij's as sorted in increasing order, is taken to be the output fuzzy set number in the (k, l) th entry of the fuzzy rule table. Spike functions are used for the output fuzzy sets, with cij's as support boundaries after they are sorted. An algorithm to compute the support boundaries explicitly without solving the matrix equation involved is included, along with a few properties of the fuzzy rule matrix for the designed fuzzy system.