• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• Structural Engineering and Mechanics
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• 제43권2호
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.

• The Journal of the Acoustical Society of Korea
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• 제17권1E호
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• pp.36-43
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• 1998

A fuzzy logic filter is constructed from a set of fuzzy IF-THEN rules which change adaptively to minimize some criterion function as new information becomes available. This paper generalizes the fuzzy logic filter and it's adaptive filtering algorithm to include complex parameters and complex signals. Using the complex Stone-Weierstrass theorem, we prove that linear combinations of the fuzzy basis functions are capable of uniformly approximating and complex continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, a complex orthogonal least-squares (COLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs. Also, we propose an adaptive algorithm based on LMS which adjust simultaneously filter parameters and the parameter of the membership function which characterize the fuzzy concepts in the IF-THEN rules. The modeling of a nonlinear communications channel based on a complex fuzzy is used to demonstrate the effectiveness of these algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.361-365
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• 2004

In this paper, we present the modeling of time series data which are corrupted by noise via nonsingleton fuzzy logic system. Nonsingleton fuzzy logic system (NFLS) is useful in cases where the available data are corrupted by noise. NFLS is a fuzzy system whose inputs are modeled as fuzzy number. The abilities of NFLS to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze corrupted time series data. In the simulation results, we compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.1163-1168
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• 2013

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied for modeling and simulation of DC characteristic of cylindrical Silicon Nanowire Transistor (SNWT). Device Geometry parameters, terminal voltages, temperature and output current were selected as the main factors of modeling. The results obtained are compared with numerical method and a good match has been observed between them, which represent accuracy of model. Finally, we imported the ANFIS model as a voltage controlled current source in a circuit simulator like HSPICE and simulated a SNWT inverter and common-source amplifier by this model.

• 한국기계가공학회지
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• 제7권4호
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• pp.128-134
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• 2008

Models of the deposition heights in the uncontrolled laser aided direct metal deposition process are constructed for the enhancement of the process integrity. Linear and non-linear statistical models as well as fuzzy model are utilized as the modeling methods. The predictability of the models are evaluated with the values of the sum of square error. The algorithm to use the models in the feedback controlled system is suggested to increase the deposition height accuracy within a layer.

• 한국지능시스템학회논문지
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• 제11권6호
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• pp.491-499
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• 2001

본 논문에서는 유전알고리즘(Genetic Algorithm)과 FCM(Fuzzy c-means) 클러스터링을 이용하여 TSK(Takagi-Sugeno-Kang)형태의 퍼지 규칙 생성과 퍼지 시스템(FCM-ANFIS)을 효과적으로 구축하는 방법을 제안한다. 구조동정에서는 먼저 PCA(Principal Component Analysis)을 이용하여 입력 데이처 성분간의 상관관계를 제거한 후에 FCM을 이용하여 클러스터를 생성하고 성능지표에 근거해서 타당한 클러스터의 수, 즉 퍼지 규칙의 수를 얻는다. 파라미터 동정에서는 유전알고리즘을 이용하여 전제부 파라미터를 최적에 가깝도록 탐색을 시도한다. 결론부 파라미터는 유전알고리즘에 의한 탐색공간을 줄이기 위해 전제부 파라미터가 결정되면 PLSE(Recursive Least Square Estimate)에 의해 추정되어진다. 이렇게 함으로서 타당한 규칙 수와 효율적인 퍼지 규칙을 얻을 수 있다. 제안된 방법의 유용성을 보이기 위해 Box-Jenkins의 가스로 데이터와 Rice taste 데이터의 모델링에 적용하여 이전의 연구보다 좋은 결과를 보임을 알 수 있었다.

• 한국항해항만학회지
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• 제31권8호
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• pp.697-703
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• 2007

비선형성이 강한 컨테이너 크레인은 작업 시에 호이스트 와이어로프의 길이와 화물의 질량 변화로 인해 더욱 복잡한 동역학적 특성을 나타낸다. 이 같은 복잡한 비선형시스템을 다루기 위해 퍼지로직이 종종 사용되는데, 특히 각 퍼지 규칙의 결론부를 상태 방정식으로 표현하는 T-S 퍼지모델이 대표적인 방법이다. 본 논문에서는 T-S 퍼지모델을 이용하여 호이스트 와이어로프의 길이나, 화물의 질량이 변화하는 환경에서도 컨테이너 크레인의 동특성을 표현할 수 있는 퍼지모델을 얻는 방법을 제안한다. 이때, 퍼지모델의 소속함수 파라미터는 RCGA가 결합된 모델조정기법을 통해 최적으로 조정된다. 이렇게 구현한 퍼지모델과 컨테이너 크레인 비선형시스템의 개루프 응답을 비교하여 그 유효성을 확인한다.

• International Journal of Aeronautical and Space Sciences
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• 제14권2호
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• pp.172-182
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• 2013

This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the Eight-Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.278-282
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• 2001

The discovery of tacit knowledge from domain experts is one of the most exciting challenges in today\`s knowledge management. The nature of decision knowledge in determining the quality a firm\`s short-term liquidity is full of abstraction, ambiguity, and incompleteness, and presents a typical tacit knowledge extraction problem. In dealing with knowledge discovery of this nature, we propose a scheme that integrates both knowledge elicitation and knowledge discovery in the knowledge engineering processes. The knowledge elicitation component applies the Verbal Protocol Analysis to establish industrial cases as the basic knowledge data set. The knowledge discovery component then applies fuzzy clustering to the data set to build a fuzzy knowledge based system, which consists of a set of fuzzy rules representing the decision knowledge, and membership functions of each decision factor for verifying linguistic expression in the rules. The experimental results confirm that the proposed scheme can effectively discover the expert\`s tacit knowledge, and works as a feedback mechanism for human experts to fine-tune the conversion processes of converting tacit knowledge into implicit knowledge.