• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.349-352
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• 1991

Fuzzy modelling is a useful method when the variation of plant dynamics is large. In the fuzzy modelling by parameter identification, a new method is proposed in the part of premise parameters identification and in expanding MISO system into MIMO system. Using the proposed method, a fuzzy model of the drum boiler of the thermal power plant can be derived. In addition, feedwater control of the drum by fuzzy controller using the fuzzy model, is simulated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1473-1476
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• 1997

This paper presents a scheme for continuous-time fuzzy modelling of nonlinear systems, based on the adjustment technique and the genetic algorithm technque. The fuzzy model is characterized by fuzzy "If-then" rules whcih represent locally linear input-output relations whose consequence part is defined as subsystem of a nonlinear system. To compute the final output and deal with the initialization and unmeasurable signal problems in on-line estimatio of the fuzzy model, a discrete-time model is obtaned. Then the parameters of both the premis and consequence of the fuzzy model are adjusted on-line by a genetic algorithm. A simulation work is carried out to demonstrate the effectiveness of the proposed method.ed method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.415-418
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• 2003

Fuzzy modelling has the approximation property far the given input-output relationship. Especially, Takagi-Sugeno fuzzy models are widely used because they show very good performance in the nonlinear function approximation problem. But generally there is not the systematic method incorporating the human expert's knowledge or experience in fuzzy rules and it is not easy to End the membership function of fuzzy rule to minimize the output error as well. The ANFIS (Adaptive Network-based Fuzzy Inference Systems) is one of the neural network based fuzzy modelling methods that can be used with various type of fuzzy rules. But in this model, it is the problem to End the optimum number of fuzzy rules in fuzzy model. In this paper, a new fuzzy modelling method based on the ANFIS and pruning techniques with the measure named impact factor is proposed and the performance of proposed method is evaluated with several simulation results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.581-584
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• 1998

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.368-373
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• 1998

This paper presents a GA-based fuzzy modelling scheme of nonlinear systems. The fuzzy model is a type of the Sugeno-Tagaki's fuzzy model whose consequence parts are described by a linear continuous dynamic equation as subsystem of a nonlinear system. The centers and width of the membership functions of the fuzzy sets defined over the input space and the orders and parameters of subsystems in the consequence parts are adjusted by a genetic algorithm. The effectiveness of the proposed method is verified

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.80-87
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• 1998

This paper presents and online scheme for fuzzy modelling of nonlinear systems, based on the model adjustment technique and the genetic algorithm technique. The fuzzy model is characterized by fuzzy "if-then" rules which represent locally linear input-output relations whose consequence parts are defined as subsystems of a nonlinear sysem. The discrete-time model for each subsystem is obtained to deal with initalization and unmeasurable signal problems in online estimation and the final output of the fuzzy model is computed from the outputs of the discrete-time models. Then, the parameters of both the premise and consequence parts of the fuzzy model are adjusted by a genetic algorithm. A set of simulation works is carried out to demonstrate the effectiveness of the proposed method.ed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.772-777
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• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술대회 논문집 전문대학교육위원
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• pp.38-41
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• 2003

An Autonomous Mobile Robot(AMR) performs duty by sensing a recognized situation and controlling suitably. The existing algorithm has some advantages that it is possible to express the obstacle exactly and the robot is sensitive to the change of environment. However, this algorithm needs to control repeatedly according to the modelling and working environment that requires a great quantity of calculations. In this paper, We supplement shortcoming and designed direction algorithm of AMR using fuzzy controller. Fuzzy controller does not derive special quality spinning expression for system, and uses rules by value expressed by language. It is used extensively to non-linear, plant which mathematical modelling is difficult etc... Fuzzy control algorithm of AMR that is used by this research applies obstacle position, distance of obstacle, Progress direction of robot, speed of robot, Perception area of sensor, etc... by fuzzy control and decide steering angle of robot.

• 한국지능시스템학회논문지
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• 제27권1호
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• pp.50-58
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• 2017

비선형 시스템 제어에 TSK 퍼지 모델을 이용하는 연구가 많다. 그러나 비선형 시스템을 TSK 퍼지 모델로 표현하기 위해서는 대상 시스템의 비선형 동특성 방정식을 알고 있거나 시스템으로부터 실험으로 입출력 공간에 충분히 분포된 데이터를 얻을 수 있어야 한다. 본 논문에서는 비선형 시스템의 TSK 퍼지 모델링에 계단 입력 응답과 GA만을 이용하는 방법을 제안한다. 연구 대상인 시스템은 제어 입력에 비선형인 경우와 출력에 비선형인 두 경우로 하였다. 제어 입력에 비선형인 경우 여러 입력 값에 대한 계단 입력 응답 데이터들로 모델링 하며, 제어 입력에 비선형인 경우에는 계단 입력 응답 데이터와 제어 입력 값이 0인 경우 응답 데이터를 이용하여 모델링 한다. 또한 제안한 방법으로 구해진 TSK 퍼지 모델로부터 퍼지 제어기를 설계하는 방법도 제시한다. 제안한 방법들을 예제들에 적용하여 모의 실험한 결과 원하는 제어 결과를 얻을 수 있었다.

• Environmental Engineering Research
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• 제23권1호
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• pp.107-113
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• 2018

Air quality and pollution have recently become a major concern; vehicle emissions significantly pollute the air, especially in large and crowded cities. There are various factors that affect vehicle emissions; this research aims to find the most influential factors affecting $CO_2$ and $NO_x$ emissions using Adaptive Neural Fuzzy Inference System (ANFIS) as well as a systematic approach. The modified ANFIS (MANFIS) was developed to enhance modelling and Root Mean Square Error was used to evaluate the model performance. The results show that percentages of $CO_2$ from trucks represent the best input combination to model. While for $NO_x$ modelling, the best pair combination is the vehicle delay and percentage of heavy trucks. However, the final MANFIS structure involves two inputs, three membership functions and nine rules. For $CO_2$ modelling the triangular membership function is the best, while for $NO_x$ the membership function is two-sided Gaussian.