• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.163.1-163
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• 2001

In this paper, we use time-frequency localization analysis method to analize the target function and the area of the target space. When we analize the function with the time and frequency axis simultaneously, the characteristic of the function is shown more precisely and the area is covered by a certain block. After we analize the target function in the time-frequency space, we can decide the activation functions and compose the hidden layer of the RBFN by choosing the radial basis function which can represent the characteristic of the target function, RBFN made by this method, designs the good structure proper to the target problem because we can decide the number of hidden node first.

• Journal of Power Electronics
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• 제4권1호
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• pp.1-11
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• 2004

A stable adaptive sensorless speed controller for three-level inverter fed induction motor direct torque control (DTC) system using the radial-basis function network (RBFN) is presented in this paper. Torque ripple in the DTC system for high power induction motor could be drastically reduced with the foregoing researches of switching voltage selection and torque ripple reduction algorithms. However, speed control performance is still influenced by the inherent uncertainty of the system such as parametric uncertainty, external load disturbances and unmodeled dynamics, and its exact mathematical model is much difficult to be obtained due to their strong nonlinearity. In this paper, the inherent uncertainty is approximated on-line by the RBFN, and an additional robust control term is introduced to compensate for the reconstruction error of the RBFN instead of the rich number of rules and additional updated parameters. Control law for stabilizing the system and adaptive laws for updating both of weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov, and the stability proof of the whole control system is presented. Computer simulations as well as experimental results are presented to show the validity and effectiveness of the proposed system.

• 제어로봇시스템학회논문지
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• 제10권2호
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• pp.185-191
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• 2004

Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권12호
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• pp.679-688
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• 2003

In order to navigate the mobile robots safely in unknown environments, many researches have been studied to devise navigational algorithms for the mobile robots. In this paper, we propose a navigational algorithm that consists of an obstacle-avoidance behavior module, a goal-approach behavior module and a radial basis function network(RBFN) supervisor. In the obstacle-avoidance behavior module and goal-approach behavior module, the fuzzy-artificial immune networks are used to select a proper steering angle which makes the autonomous mobile robot(AMR) avoid obstacles and approach the given goal. The RBFN supervisor is employed to combine the obstacle-avoidance behavior and goal-approach behavior for reliable and smooth motion. The outputs of the RBFN are proper combinational weights for the behavior modules and velocity to steer the AMR appropriately. Some simulations and experiments have been conducted to confirm the validity of the proposed navigational algorithm.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.237-240
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• 2005

This paper describer a design expert system which determines the design values of shadow mask using Case-Based Reasoning. In Case-Based Reasoning, it is important to both retrieve similar cases and adapt the cases to meet the design specifications exactly. Especially, the difficulty in automating the adaptation process will prevent the designers from using the design expert systems efficiently and easily. This paper explains knowledge-based design support systems for shadow mask through neural network-based case adaptation. Specifically, we developed 1) representing design knowledge and 2) adaptive case-based reasoning method using RBFN (Radial Basis Function Network).

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 한국통신학회논문지
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• 제22권5호
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• pp.1026-1035
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• 1997

본 연구는 RBFN의 일종인 GPFN과 PNN을 이용한 음소인식에 관한 연구이다. RBFN의 구조는 계층형 신경망의 구조와 유사하지만, hidden층에서 활성화함수, 참조벡터 및 학습알고리듬의 선택이 다르다. 특히 PNN은 시그모이드 함수가 지수를 포함한 함수들의 한 분류로 대체된다는 것이며, 학습이 필요없으므로 전체계산 시간이 빠르게 수행된다. 5모음, 12자음을 대상으로 한 음소인식 실험에서 평가데이터, VQ와 LVQ에 의한 코드북 데이터를 사용한 경우에 음성의 통계적 특성을 잘 반영하고 있는 RBFN의 일종인 GPFN과 PNN의 인식결과가 MLP보다 우수하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권3호
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• pp.145-155
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• 2002

내용 기반 영상 검색에서 대부분의 기존 방법들은 서로 다른 특징들 사이의 선형 관계를 가정하고 또 사용자가 직접 각 특징의 가중치를 설정하도록 한다 허나 특징들 사이의 관계가 선형적으로 가정된 하에서는 고차원의 개념과 인간의 지각 주관성을 충분히 표현할 수 없는 단점이 있다. 본 논문에서는 신경망에 기반한 영상 검색 모델이 제안된다. 이는 RBFN을 이용한 내용 기반 영상 검색 기법과 인간컴퓨터 상호작용의 접근 방법을 기반으로 구축되었다. RBFN을 이용하여 특징들 사이의 비선형적 관계를 추출해낼 수 있고 사용자가 처음에 질의 영상을 선택하고 관련성 피드백을 통하여 점차적으로 목표 영상을 찾아나가도록 함으로써 영상의 비교를 더 정확하게 할 수 있다. 실험은 145개의 클래스로 구분되며 1,015개의 영상을 포함하는 데이타베이스를 사용하여 재생과 정도를 계산하였다. 실험 결과는 제안된 방법의 재생과 정도가 각각 93.45%과 80.61%로서, 기존의 선형 결합 방법이나 순위 기반 방법 그리고 역전파 알고리즘에 기반한 방법보다 더 뛰어난 검색 성능을 지님을 보여준다.

• 한국콘텐츠학회논문지
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• 제5권6호
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• pp.41-48
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• 2005

이 논문은 얼굴인식을 수행하기 위해서 이미 잘 알려진 주성분 분석법과 선형판별 분석법에 레이디얼 기저 함수 신경망을 결합한 인식 알고리즘을 제시하였다. 입력된 원래의 얼굴영상은 주성분분석법을 통하여 차원을 줄인 고유 얼굴 가중치를 산출한다. 이 가중치 벡터를 선형판별 분석법의 입력데이터로 사용하여 선형판별분석의 변환행렬을 계산할 때 클래스 내의 분산행렬에서 특이점이 발생하지 않도록 하면서 특징벡터를 산출하여 인식을 수행하였다. 두 번째 시도에서는 선형판별분석법에 의해 생성된 특징벡터를 레이디얼 기저 함수 신경망에 입력하여 학습하고 얼굴인식을 수행하였다. ORL DB의 얼굴영상에 대해 실험한 결과 93.5%의 인식률을 얻을 수 있었다.