• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7277-7282
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• 2014

In this study, the gradient descent algorithm was used for FLC analysis and the algorithm was used to represent the effects of nonlinear parameters, which alter the antecedent and consequence fuzzy variables of FLC. The controller parameters choose the control variable by iteration for gradient descent algorithm. The FLC consists of 7 membership functions, 49 rules and a two inputs - one output system. The system adopted the Min-Max inference method and triangle type membership function with a 13 quantization level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.730-737
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• 2001

In this paper, an stabilization control method using adaptive neuro-fuzzy controller(ANFC) is proposed for modeling of nonlinear complex systems. The proposed adaptive neuro-fuzzy controller implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks from input and output data of processes. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.4 s.44
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• pp.55-66
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• 2005

The effectiveness of fuzzy supervisory control technique for the control of seismic responses of smart base isolation system is investigated in this study. To this end, first generation base isolated building benchmark problem is employed for the numerical simulation. The benchmark structure under consideration is an eight-story base isolated building having irregular plan and is equipped with low-damping elastometric bearings and magnetorheological (MR) dampers for seismic protection. Lower level fuzzy logic controllers (FLC) for far-fault or near-fault earthquakes are developed in order to effectively control base isolated building using multi-objective genetic algorithm. Four objectives, i.e. reduction of peak structural acceleration, peak base drift, RMS structural acceleration and RMS base drift, are used in multi-objective optimization process. When earthquakes are applied to benchmark building, each of low level FLCs provides different command voltage and supervisory fuzzy controller combines two command voltages io one based on fuzzy inference system in real time. Results from the numerical simulations demonstrate that base drift as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.402-409
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• 2007

In this paper recognized for trajectory using Condensation algorithm. In this pater used fuzzy controller for recognized trajectory using fuzzy reasoning. The fuzzy system tract to the three-dimensional space for raw and roll movement. The joint angle ${\theta}_1$ of the manipulator rotate from $0^{\circ}\;to\;360^{\circ}$, and the joint angle ${\theta}_2$ rotate from $0^{\circ}\;to\;180^{\circ}$. The moving object of velocity display for recognition without error using Condensation algorithm. The tracking system demonstrated the reliability of proposed algorithm through simulation against used trajectory.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.52-62
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• 2006

In a fuzzy control system to process fuzzy data in high-speed for intelligent systems, one of the important problems is the improvement of the execution speed in the fuzzy inference and defuzzification stages. Especially, it is more important to have high-speed operations in the consequent part and defuzzification stage. Therefore, in this paper, to improve the speedup of the fuzzy controllers for intelligent systems, we propose an integer line mapping algorithm using only integer addition to convert [0,1] real values in the fuzzy membership functions in the consequent part to integer grid pixels $(400{\times}30)$. This paper also shows a novel defuzzification algorithm without multiplications. Also we apply the proposed system to the truck backer-upper control system. As a result, this system shows a real-time very high speed fuzzy control as compared as the conventional methods. This system will be applied to the real-time high-speed intelligent systems such as robot arm control.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.531-534
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• 1999

벡터제어기법은 유도전동기의 무부하 시험 및 구속시험 등을 통하여 구한 고정자 및 회전자 상수를 이용한 전동기의 수학적 모델을 기초로 하여 이루어진다. 따라서 유도전동기의 수학적 모델을 구성하는 고정자 및 회전자 상수의 정확성은 곧 벡터제어의 성능과도 직결된다. 하지만, 온도 상승 등의 영향으로 회전자 저항값은 정상치보다 최대 80∼100%까지 상승 할 수 있으며, 이는 벡터제어의 특성을 저하시키는 요인이 된다. 따라서, 본 연구에서는 회전자 저항을 이용하는 자속 PI제어부를 회전자 저항을 사용하지 않는 자속 퍼지제어부로 대체하고 측정한 3상 전류를 이용하여 회전자 저항값의 변화를 실시간으로 추정·보상하는 퍼지추론기를 구성하므로써 회전자 저항의 변화에도 최적의 효율 및 성능을 가지는 보완된 벡터 제어 기법을 개발하였다.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2995-2997
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• 2000

원자력발전소의 열출력 계산 결과에 가장 큰 영향을 미치는 변수는 주급수 유량이며, 측정방식상의 특성(Venturi Fouling)으로 인해 계산시 과다하게 반영될 소지가 있다 본 연구에서는 이 측정 오차를 최소화하기 위하여 뉴로-퍼지 논리를 이용하여 주급수 유량을 예측한 후 그 결과를 통해 열출력을 재평가하고자 하였다. 즉, 뉴로-퍼지로의 입력 변수(증기발생기 압력 및 수위. 터빈 충동실 압력)들은 모의훈련으로 출력을 상승시키면서 취득한 후 Wavelet Denoising 기법을 이용하여 노이즈를 제거시키고. 뉴로-퍼지 추론 계통의 파라메타들을 최적화시키기 위하여 유전적 알고리듬 및 최소자승법에 의한 Hybrid Learning Rule을 이용하여 학습시켰다. 시뮬레이션을 수행한 결과, 주급수 유량이 양호하게 예측되어, 이 결과를 토대로 열출력을 평가하는데 본 알고리듬의 적용이 성공적임을 입증하였다.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.159-164
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• 2006

In recent, the marine engine of a large size is being realized a lower speed, longer stroke and a small number of cylinders for the energy saving. Consequently the variation of rotational torque became larger than former days because of the longer delay-time in fuel oil injection process and an increased output per cylinder. It was necessary that algorithms have enough robustness to suppress the variation of the delay-time and the parameter perturbation. This paper shows the structure of hybrid F-NFC against the delay-time and the perturbation of engine parameter as modeling uncertainties, and the design of the robust speed controller by hybrid F-NFC for the engine. And, The Parameter values of linear equation are determined by RC-GA for F-NFS. The hybrid F-NFC is combined the F-NFC and PID controller for filling up each.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.83-93
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• 2000

This paper proposes the use of the analytic hierarchy process(AHP) controller for obtaining optimal cycle time of traffic intersection signal. By means of simulations that have data from expert traffic estimators, the performance of AHP controller is compared with a fixed-time controller and fuzzy controller. A simulation was executed using data obtained from many expert interviewer of Ansan City hall rotary. The principal issue is how AHP controller algorithm can aid traffic signal supervisor to construct an expert system for traffic intersection signal control. This paper has two steps: (I) we shall present a new methodology based on the work of Satty for calculating a scale of importance of each of the objectives, (2) we also consider that the human's decision mechanism is represented by approximating reasoning where the fuzzy integral is used.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.79-90
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• 2007

In this paper we proposed and implemented an incident detection model which combines fuzzy algorithm and traffic pattern in order to enhance the efficiency of incident detection for the highways with lamps. Most of the existing algorithms dealt with highways without lamps and can not be used for detecting incidents in the highways with lamps. The data used for model building are traffic volume, occupancy, and speed data. They have been collected by a loop sensor at 5 minutes interval at a point in the Internal Circular Highway of Seoul for the period of 3 months. In this model, the three parameters collected by sensor were fuzzified and combined with the daily traffic pattern of the link. The test of efficiency of the propsed model was performed by comparing the result of proposed model with traditional APID algorithm and fuzzy algorithm without the pattern data respectively. The result showed significant amount of improvement in reducing the false incident detection rate by 18%.