• 조명전기설비학회논문지
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• 제25권9호
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.135-138
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• 1997

In this paper, we proposed effective modeling method to nonlinear complex system. Fuzzy Neural Network(FNN) was used as basic model. FNN was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, we used FNN which was proposed by Yamakawa. The FNN used Simple Inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. This structure has better property than other structure at learning speed and convergence ability. But it has difficulty at definition of membership function. We used Hard c-Mean method to overcome this difficulty. To evaluate proposed method. We applied the proposed method to waste water treatment process. We obtained better performance than conventional model.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.437-440
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• 1998

In this paper, the design method of fuzzy neural network(FNN) controller using indirect adaptive control technique is presented for controlling chaotic nonlinear systems. Firstly, the fuzzy model identified with a FNN in off-line process. Secondly, the trained fuzzy model tunes adaptively the control rules of the FNN controller in on-line process. In order to evaluate the proposed control method, Indirect adaptive control method is applied to the representative continuous-time chaotic nonlinear systems, that is, the Duffing system and the Lorenz system. Simulations are done to verify the effectivencess of controller.

• 조명전기설비학회논문지
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• 제20권10호
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• pp.47-57
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• 2006

본 논문은 ALM-FNN 제어기와 ANN 제어기를 사용하여 SynRM 드라이브의 최대토크 제어를 제시한다. 이 제어기는 인버터의 정격 전류와 전압 제한을 고려하고 전 속도 영역에 적용된다. 각 제어모드를 위하여 최대토크를 위한 최적의 d-축 전류 $^i{_d}$를 결정한다. 제시된 제어 알고리즘은 ALM-FNN 제어기와 ANN 제어기로 SynRM 드라이브 시스템을 제어하는데 적용된다. 최대토크 제어에 의하여 제어된 동작 특성은 실험을 통하여 상세히 설명한다. 또한 본 눈문은 ALM-FNN 제어기와 ANN 제어기 결과분석을 통하여 타당성을 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.390-393
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• 1997

Fuzzy logic and neural networks are complemetary technologies in the design of intelligent system. Fuzzy neural network(FNN) as an auto-tuning method is actually known to an excellent method for the adjustment of the fuzzy rule. However, this has a weak point, because the convergence to the optimum depends on the initial condition. In this paper we develop a coding format to determine a FNN model by chromosome in GA and present systematic approach to identify the parameters and structure of FNN. The proposed hybrid tuning method realizes to construct minimal and optimal structure of the fuzzy mode simultaneously and automatically. This paper shows effectiveness of the tuning system by simulations compared with conventional methods.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.987-992
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• 1998

Solar cell has an optimum operating point to extract maximum power. To control operating point of the solar cell, a fuzzy controller has already been proposed by our research group. However, several parameters are determined by trial and error. To overcome this problem, this paper adopts Fuzzy Neural Network (FNN) for maximum power point tracking control for photovoltaic array. The FNN can be trained to perfect fuzzy rules and to find an optimum membership functions on-line.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.143-146
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for speed control of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions.

• 한국항해학회지
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• 제18권2호
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• pp.69-81
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model, but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics at low speed. In this paper, the authors propose a new berthing control system which can evaluate as closely as cap-tain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS-90 MK Ⅲ) and represent the ship motion characteristics internally. According to learning procedure, both FNN controllers can tune membership functions and identify fuzzy control rules automatically. The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.

• 조명전기설비학회논문지
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• 제24권4호
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• pp.33-42
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• 2010

벡터제어를 적용한 유도전동기 드라이브는 고성능 제어를 위하여 산업 적용분야에 광범위하게 사용되고 있다. 그러나 유도전동기의 모델은 비선형이고 복잡하기 때문에 포화, 온도변화, 외란 및 파라미터 변동등에 의해 성능 및 신뢰성이 저하된다. 이러한 가변속 드라이브를 제어하기 위하여 종래의 PI와 같은 제어기들이 일반적으로 사용되어졌다. 이러한 제어기들은 이상적인 벡터제어 상태에서도 광범위한 동작영역에서 양호한 성능을 나타내는데 한계를 가지고 있다. 본 논문은 퍼지제어, 신경회로망, 적응 퍼지제어로 구성된 FNN(Fuzzy-Neural Network)-PI 제어기 기반 자기동조 PI 제어기와 ANN을 이용한 속도추정을 제시한다. FNN-PI, AFC, ANN 제어기를 이용한 제어 알고리즘은 유도전동기 드라이브 시스템에 적용하여 그 결과를 분석하고 제어기의 효용성을 입증한다.