• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.259-262
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian menbership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• 한국지능시스템학회논문지
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• 제12권2호
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• pp.144-150
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• 2002

본 논문에서는 지연시간을 갖는 비선형 시스템의 효율적 제어를 위해 퍼지-신경망에 기반한 지연시간 보상기를 제안하였다. 제안된 제어시스템은 ANFIS(Adaptive Neuro-Fuzzy Inference System)라고 불리는 두개의 퍼지-신경망으로 구성되며 이중 하나는 직-병렬 방식으로 동작하고 다른 하나는 병렬 방식으로 동작한다. 직-병렬 방식으로 동작하는 퍼지-신경망은 지연시간을 갖는 비선형 시스템의 응답을 추종하는 특성을 갖으며 병렬 방식으로 동작하는 퍼지-신경망은 지연시간을 보상하기 위한 시스템 출력을 예측하는 기능을 수행한다. 따라서 본 연구에서 제안된 시스템은 전형적인 Smith 예측기의 비선형 시스템에의 적용을 위한 확장이라고 생각할 수 있다. 본 논문에서는 제안된 지연시간 보상기의 상세한 설계과정을 보였으며 또한 제안된 제어기 설계 기법의 유용성 화인을 위해 비선형 수치데이터에 대한 컴퓨터 모의실험을 수행하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• 제어로봇시스템학회지
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• 제1권3호
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• pp.109-117
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• 1995

이 글에서는 neuro-fuzzy control system에 관심을 가지신 control engineer들에게 다소나마 보탬이 되고자 neuro-fuzzy control system을 간단히 소개한 후 이 system이 실제로 어떻게 응용되고 있는지 살펴보고자 한다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.201-208
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• 2005

Neuro-Fuzzy modeling approach is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system consists of friction pendulum systems (FPS) and a magnetorheological (MR) damper. Fuzzy model of the M damper is trained by ANFIS using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses or experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• 한국정밀공학회지
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• 제14권4호
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• pp.46-55
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• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• 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.

• 제어로봇시스템학회논문지
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• 제22권1호
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• pp.40-45
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• 2016

This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

• 제어로봇시스템학회논문지
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• 제7권11호
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• pp.958-961
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• 2001

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 is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.