• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.675-678
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• 1997

In this paper, we present a fast and robust algorithm for the design of fuzzy controller and identifying fuzzy model from numerical data by combining the cluster estimation method with a linear least squares estimation procedure. The proposed method is compared with Adaptive Neuro-Fuzzy Inference System(ANFIS) as the standard example of neuro-fuzzy model. Finally we will show its usefulness and effectiveness for the design of fuzzy controller of a cart-pole system and fuzzy modeling for the coagulant dosing of a water purification system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.691-695
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• 1998

Most of systems has nonlinearity . And also accurate modelings of these uncertain nonlinear systems are very difficult. In this paper, a fuzzy modeling technique for the stabilization control of an IP(inverted pendulum) system with nonlinearity was proposed. The fuzzy modeling was acquired on the basis of ANFIS(Adaptive Neuro Fuzzy Infernce System) which could learn using a series of input-output data pairs. Simulation results showed its superiority to the PID controller. We believe that its applicability can be extended to the other nonlinear systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2018-2020
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• 2001

In this study, a Neuro-Fuzzy Controller which has the characteristic of Fuzzy control and Artificial Neural Network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to Fuzzy rules are created by an expert. To adapt the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in the Neuro-Fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of an algorithm designed above, an operating characteristic of a AC servo motor is investigated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• ETRI Journal
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• 제32권4호
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• pp.530-539
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• 2010

As the conventional silicon metal-oxide-semiconductor field-effect transistor (MOSFET) approaches its scaling limits, quantum mechanical effects are expected to become more and more important. Accurate quantum transport simulators are required to explore the essential device physics as a design aid. However, because of the complexity of the analysis, it has been necessary to simulate the quantum mechanical model with high speed and accuracy. In this paper, the modeling of double gate MOSFET based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The ANFIS model reduces the computational time while keeping the accuracy of physics-based models, like non-equilibrium Green's function formalism. Finally, we import the ANFIS model into the circuit simulator software as a subcircuit. The results show that the compact model based on ANFIS is an efficient tool for the simulation of nanoscale circuits.

• 한국지능시스템학회논문지
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• 제10권5호
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• pp.417-422
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• 2000

본 논문에서는 적응 뉴로-퍼지 모델링을 위해 최근에 BBS(blind source separation)분야에서 발전된 독립 성분 분석기법(ICA)을 전처리로 이용하여 효과적인 퍼지 규칙을 생성하는 방법을 제안한다. 기존의 뉴로-퍼지 모델링은 입력 데이터 성분간의 상관관계를 고려하지 않고 입력공간을 분할하기 때문에 효과적으로 분할하지 못하는 단점이 있다. 이로 인해 과도한 규칙 수와 큰 오차를 가지고 있었다. 이에, 본 연구에서는 독립 성분 분석기법을 이용하여 입력 데이터 성분간의 상관관계를 제거함으로서 적은 규칙 수를 갖으면서도 효율적인 퍼지 규칙을 얻을 수 있도록 하였다. 시뮬레이션 예로서 Box-Jenkins의 가스로 데이터의 모델링에 적용하여 유용성과 제안된 방법이 이전의 연구보다 좋은 결과를 보임을 알 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.443-448
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• 1998

As the strip caster system that produces a regular steel plate can be considered as a complicate nonlinear multi-variable system, it is not easy to obtain an effective control system. One way to overcome the difficulties is to apply the intelligent neuro-fuzzy fusion approach in developing the control scheme. The neuro-fuzzy control scheme possesses several distinct advantages, including the fact that it doesn't need the exact mathematical modeling of controlled plant and can provided some robustness in the control scheme. In this paper, an intelligent neuro-fuzzy for the stripe caster system will be proposed. The effectiveness of the proposed scheme will be demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• International Journal of Korean Welding Society
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• 제1권2호
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.