• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 춘계종합학술대회
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• pp.494-497
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• 2002

본 논문에서는 동적신경회로망을 이용한 미지의 비선형 시스템 제어 방식을 제안하였다. 제안한 방식은 비선형 시스템의 상태 공간 모델과 유사한 형태의 신경회로망을 구성하여 비선형 시스템을 식별하고, 식별한 정보를 이용하여 제어기를 설계하는 방식이다. 제안한 방식의 유용성을 확인하기 위하여 단일 관절 매니퓰레이터를 대상으로 시뮬레이션을 수행한 결과 우수한 제어 성능을 확인하였다.

• 한국항해항만학회지
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• 제30권4호
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• pp.259-265
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• 2006

This paper presents an improved adaptive neural network autopilot based on our previous study for track-keeping control of ships. The proposed optimal neural network controller can automatically adapt its learning rate and number of iterations. Firstly, the track-keeping control system of ships is described For the track-keeping control task, a way-point based guidance system is applied To improve the track-keeping ability, the off-track distance caused by external disturbances is considered in learning process of neural network controller. The simulations of track-keeping performance are presented under the influence of sea current and wind as well as measurement noise. The toolbox for track-keeping simulation on Mercator chart is also introduced.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2341-2344
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• 2001

In recent years the neural network known as a sort of the intelligent control strategy is used as a powerful tool for designing control system since it has learning ability. But it is difficult for neural network controllers to guarantee the stability of control systems. In this paper we try connecting a radial basis function network to an adaptive control strategy. Radial basis function networks are simpler and easier to handle than multilayer perceptrons. We use the radial basis function network to generate control input signals that are similar to the control inputs of adaptive control using linear reparameterization of the robot manipulator. We adopt the saturation function as an auxiliary controller. This paper also proves mathematically the stability of the control system under the existence of disturbances and modeling errors.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 춘계합동학술대회 논문집
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.231-234
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• 1993

This paper deals with the design of an adaptive controller using neural network. We present RBFMLP Neural Network which consists of serial-connected two networks - Radial Basis Function Network and Multi Layer Perceptron, and then design a controller based on proposed networks with the adaptive control system structure, The plant and parameters of the controller are identified by the neural networks. We use the dynamic backpropagation algorithm for the learning of networks. Simulations represent the superiorities of the proposed network and the controller.

• 한국산학기술학회논문지
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• 제10권3호
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• pp.542-546
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• 2009

본 논문은 신경망을 이용한 간섭 신호 제어로써 합성 다층 퍼셉트론에 입각하여 셀룰라 이동통신에서의 수신된 신호들을 역전파 학습알고리즘을 이용하여 검파하는 것에 대하여 소개하였다. 그리고 컴퓨터 시뮬레이션 결과를 통하여 co-channel간섭과 협대역 간섭의 실제 음색에서 기존에 쓰여진 Rake수신기보다 더 낮은 비트 오차 확률을 가지는 NNAC(neural network adaptive correlator)에 대하여 분석 고찰하였다.

• 융합신호처리학회논문지
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• 제5권2호
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• pp.132-137
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• 2004

본 논문에서는 다층 신경회로망의 구조를 가지며, 백프로퍼게이션 학습 알고리즘을 이용한 적응신호처리 시스템을 구현하였다. 최소자승 알고리즘을 이용한 적응 잡음 제거기는 기준 신호와 잡음과의 상관도에 영향을 많이 받고, 정보 신호가 잡음에 비하여 상대적으로 작은 경우에 한계를 보이고 있다. 이와 같은 잡음에 대하여 본 논문에서 제안된 시스템은 좋은 성능을 보인다. 또한, 은닉층의 수와 노드 수를 다르게 구성했을 경우에 시스템의 출력에 미치는 결과에 대하여 분석하였다. 제안된 적응 신호처리 시스템의 장점을 알아보기 위하여 성능 평가의 기준이 되는 최소자승 알고리즘을 이용한 시스템과 비교하였다.

• 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.

• 한국정밀공학회지
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• 제21권10호
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• pp.65-77
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• 2004

In this paper, we propose an adaptive mechanism based on immune algorithm and neural network identifier technique. It is also applied fur an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To solve this problem, we use the neural network identifier (NNI) technique fur modeling the plant and humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using an immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. Finally, the simulation and experimental result fur the control of steering and speed of AGV system illustrate the validity of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

• Journal of Communications and Networks
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• 제15권3호
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• pp.301-311
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• 2013

This paper proposes a policy-based quality of service (QoS) management framework for adaptive routing decisions. We present an approach considering interior gateway protocol (IGP) for path discovery mechanisms and QoS-aware policies for configuring the network elements. The integration of the aforementioned modules into this policy-based network management (PBNM) system is demonstrated by conducting experiments in a real environment, the hellenic public administration network SYZEFXIS. These experiments combine different traffic conditioning mechanisms through event detectors, consider IP service level agreement mechanisms that interoperate with the PBNM system and analyze the enforcement of IGP and QoS policies. Finally, validation and measurement tools are used to prove the efficiency of this framework. It is shown that this architecture offers significantly increased performance and learning capabilities, while the PBNM system achieves adaptive QoS routing through automated configuration considering the avoidance of suboptimal routing issues or under-performance conditions of the network entities.