• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.200-205
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• 1993

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type 1), linear inference (type 2), and modified linear inference (type 3). The fuzzy c-means clustering and modified complex methods are used in order to identify the preise structure and parameter of fuzzy implication rules, respectively and the least square method is utilized for the identification of optimal consequence parameters. Time series data for gas funace and sewage treatment processes are used to evaluate the performances of the proposed rule-based fuzzy modeling.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

• 전기학회논문지
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• 제57권9호
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• pp.1642-1651
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• 2008

The dynamic behavior of power systems is affected by the interactions between linear and nonlinear components. To analyze those complicated power systems, the linear approaches have been widely used so far. Especially, a synchronous generator has been designed by using linear models and traditional techniques. However, due to its wide operating range, complex dynamics, transient performances, and its nonlinearities, it cannot be accurately modeled as linear methods based on small-signal analysis. This paper describes an application of the Takaki-Sugeno (T-S) fuzzy method to model the synchronous generator in a single-machine infinite bus (SMIB) system. The T-S fuzzy model can provide a highly nonlinear functional relation with a comparatively small number of fuzzy rules. The simulation results show that the proposed T-S fuzzy modeling captures all dynamic characteristics for the synchronous generator, which are exactly same as those by the conventional modeling method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.277-283
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• 2013

In this study, we propose a novel method for modeling dynamic hysteresis. Hysteresis is a widespread phenomenon that is observed in many physical systems. Many different models have been developed for representing a hysteretic system. Among them, the Duhem model is a classical nonlinear dynamic hysteresis model satisfying the properties of hysteresis. The purpose of this work is to develop a novel method that expresses the local dynamics of the Duhem model by a linear system model. Our approach utilizes a certain type of fuzzy system that is based on Takagi-Sugeno (T-S) fuzzy models. The proposed T-S fuzzy Duhem model is achieved by fuzzy blending of the linear system model. A simulated example applied to shape memory alloy actuators, which have typical hysteretic properties, illustrates the applicability of our proposed scheme.

• 사물인터넷융복합논문지
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• 제4권1호
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• pp.1-5
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• 2018

본 논문에서는 노이즈에 의해 훼손된 시계열 데이터의 모델링에 대하여 다룬다. 모델링 기법으로, 논싱글톤 퍼지 시스템을 사용한다. 논싱글톤 퍼지 시스템의 주요특징은 미지의 비선형시스템의 입력이 퍼지값으로 모델링 된다는데 있다. 그러므로 퍼지시스템에 인가되는 학습데이터나 입력데이터 등이 노이즈나 외부 환경에 의해 변형된 경우에 매우 유용하게 적용될 수 있다. 성능비교를 위해 벤치마크 데이터로 잘 알려진 Mackey-Glass 데이터를 사용한다. 이들 데이터 모델링을 통하여 결과를 비교, 분석하여 논싱글톤 퍼지시스템이 잡음에 대하여 보다 강인하고 효율적임을 본 논문에서 보인다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.149-152
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• 2000

This paper addresses the wavelet fuzzy modeling using Virus-Evolutionary Genetic Algorithm (VEGA). We build a fuzzy system model which is equivalent to the wavelet transform after identifying the coefficients of wavelet transform. We can obtain an accurate system model with a small number of coefficients due to the energy compaction property of the wavelet transform. It thus means that we can construct a fuzzy system model with a small number of rules. In order to identify the wide-ranged coefficients of the wavelet transform, VEGA is adopted, which has prominent ability to avoid premature local convergence that is suitable to complex optimization problems. We demonstrate the superiority of our proposed fuzzy system modeling method over the previous results by modeling nonlinear function.

• 통합자연과학논문집
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• 제13권3호
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• pp.97-104
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• 2020

This study suggests the emotion space modeling and emotion inference methods suitable for personalized services based on psychological and emotional models. For personalized emotion space modeling taking into account the subjective disposition based on the empirical assessment of the personal emotions felt by the personalization process of emotion space was used as a decision support tool, the Analytic Hierarchy Process. This confirmed that the special learning to perform personalized emotion space modeling without considering the subjective tendencies. In particular to check the possible reasoning based on fuzzy emotion space modeling and sensitivity for the quantification and vague human emotion to it based on the inherent human sensitivity.

• 한국지능시스템학회논문지
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• 제10권6호
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

• 한국지능시스템학회논문지
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• 제6권2호
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• pp.81-89
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• 1996

퍼지모델링의 설계 방법을 교통경로선택의 모델동정을 위하여 제안한다. 제안된 퍼지모델은 최적화이론, 퍼지구현규칙을 사용하여 ""IF..., THEN...""의 효율적인 형태로 시스템구조와 파라미터 동정을 시행한다. 이 논문에서 간략추론, 선형추론, 병형된 선형추론의 3가지종류의 퍼지모델링 방법을 제시한다. 이 퍼지추론 방법은 인간의 교통행동의 정확한 추정과 정밀한 묘사를 위해 교통경로선택 모델을 개발하기 위해 이용된다. 퍼지규칙의 전반부 구조와 파라미터를 동정하기 위해 개선된 컴플렉스법을 사용하고, 최적후반부 파라미터를 동정하기 위해 최소자승법이 사용된다. 교통경로선택 데이타가 제안된 퍼지모델 성능을 평가하기 위해 사옹된다. 제안된 방법이 기존의 다른 연구들 - 즉 BL, PS, FL, NN, FNNs 모델 등 - 보다 더 높은 정확도를 가진 퍼지모델을 생성함을 보인다. 생성함을 보인다.