• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• 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.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.650-652
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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 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 them. The resultant rule base is Macvicar-Whelan type. The frequency response information is used in tuning of membership functions. Also a tuning strategy for the scaling factors is Proposed based on the relationship between PID gain and them. Simulation results show better performance and the effectiveness of the proposed method.

• 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 Korean Society for Technology of Plasticity Conference
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• 1998.06a
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• pp.47-56
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• 1998

Designing Trim die block is a complicated and time-consuming process heavily resting on the experience of the designer. To reduce design time and human errors, a knowledge base is used to automated the design process. In this paper, a framework of the Rule-based CAD System is presented for trim die block design consisting of a rule-base, design process control module and geometric modeler (CATIA). The rule-base includes design rules and know-how of design specialist. CATIA is used as the overall CAD environment and rule-base and design control modules are developed by C++ language with an interface to CATIA. Using the rule-base, the designer can explore alternating designs fast by changing design parameters and the part-list is automatically created which avoids miscommunication.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.12
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• pp.566-574
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• 2001

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

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1149-1152
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• 1999

In this paper we analyzed each region of specific points and ｅ-Δｅphase plane in order to make fuzzy rule base. After we composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. The composed method are converged more rapidly than single rule base in convergence region. Proposed method is alternately use at specific points of ｅ-Δｅphase plane with two fuzzy control rules, that is one control rule occruing the steady state error used in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Two fuzzy control rules in the ｅ-Δｅ phase plane decide the change time according to response characteristics of plants. As the results of simulation through the second order plant and the delay time plan, Proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.1003-1006
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• 2003

This paper proposed An auto fuzzy rule-base extraction method using genetic algorithm. The suggested method is an auto fuzzy rule-base extration method neither expert advise fuzzy rule-base nor trial and error fuzzy rule-base. In order to confirm the validity of proposed method, we have applicated dc motor control and confirmed effective.

• Journal of the military operations research society of Korea
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• v.2 no.1
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• pp.177-184
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• 1976

As the organizational size of a military service or business increases and its management becomes complex, the success in its management depends less on static type of management but more on careful, dynamic type of management. In this thesis, an operations research technique is applied to the problems of determining optimal air traffic control rule and of optimal capacity of air base for a military air base. An airport runway is regarded as the service facility in a queueing mechanism, used by landing, low approach, and departing aircraft. The usual order of service gives priority different classes of aircraft such as landings, departures, and low approaches; here service disciplines are considered assigning priorities to different classes of aricraft grouped according to required runway time. Several such priority rules are compared by means of a steady-state queueing model with non-preemptive priorities. From the survey conducted for the thesis development, it was found that the flight pattern such as departure, law approach, and landing within a control zone, follows a Poisson distribution and the service time follows an Erlang distribution. In the problem of choosing the optimal air traffic control rule, the control rule of giving service priority to the aircraft with a minimum average waiting cost, regardless of flight patterns, was found to be the optimal one. Through a simulation with data collected at K-O O Air Base, the optimal take-off interval and the optimal capacity of aircraft to be employed were determined.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.441-446
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• 1992

We propose a control architecture for the fuzzy logic control of robot manipulators and a rule base derivation method for a fuzzy logic controller(FLC) using a neural network. The control architecture is composed of FLC and PD(positional Derivative) controller. And a neural network is designed in consideration of the FLC's structure. After the training is finished by BP(Back Propagation) and FEL(Feedback Error Learning) method, the rule base is derived from the neural network and is reduced through two stages - smoothing, logical reduction. Also, we show the performance of the control architecture through the simulation to verify the effectiveness of our proposed method.