• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.95.4-95
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• 2001

In this paper, we proposed and analyze an robust adaptive control scheme for uncertain nonlinear systems using Universal function approximators. The proposed scheme completely overcomes the singularity problem which occurs in the indirect adaptive feedback linearizing control. No projection in the estimated parameters and no switching in the control input are needed. The stability of the closed-loop systems is guaranteed in the Lyapunov standpoint.

• 전산구조공학
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• 제10권2호
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• pp.265-272
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• 1997

본 논문은 신경망 근사 해석 모델 개발을 궁극적인 목적으로 하는 기초적 연구로서, 기존의 수치해석 알고리즘과의 성능 비교를 통하여 신경망 알고리즘의 특성과 역할을 수치해석의 관점에서 정확히 판단하는데 목적이 있다. 신경망 알고리즘을 변형하여 선형 연립 방정식의 해를 구하는 두가지 방법을 제안하였고, 회귀분석, 보간법과의 비교를 통하여 광범위한 근사자(universal approximator)로서의 역할을 보였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1414-1417
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• 1993

Wang and Medel proved (1991) that fuzzy systems with product inference, centroid defuzzification, and everywhere positive membership functions (in particular, Gaussians, Wang, 1992) are capable of approximating any real continuous control function on a compact set to arbitrary accuracy. Kosko (1992) proved that fuzzy systems, in which membership functions have compact support, and combination operation (V-operation) for rules is the sum, are also universal approximators. In this paper, we generalize this result of Kosko and prove that for any &- and V-operations, any defuzzification procedure, and any basic membership function with a compact support, the resulting fuzzy controls are universal approximators. Also, Wang's result is transfered to min-inference.

• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.131-141
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• 2003

In this paper, we prove that multi-variate fuzzy polynomials are universal approximators for multi-variate fuzzy functions which are the extension principle of continuous real-valued function under $T_W-based$ fuzzy arithmetic operations for a distance measure that Buckley et al.(1999) used. We also consider a class of fuzzy polynomial regression model. A mixed non-linear programming approach is used to derive the satisfying solution.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.40.4-40
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• 2001

This paper addresses the problem of designing robust adaptive output tracking control for a class of MIMO nonlinear systems which have different number of inputs and outputs The stability of the whole closed-loop system is guaranteed in the sense of Lyapunov and uniformly Itimately boundedness of the tracking error vector as well as estimated parameters are shown. In addition, we show that the restrictive assumptions on input gain matrix which is presumed in the past works can be eliminated by using proposed control law.

• International Journal of Control, Automation, and Systems
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• 제4권2호
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• pp.178-186
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• 2006

We propose and analyze a robust adaptive fuzzy controller for nonlinear systems without a priori knowledge of the sign of the input gain function. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. The uncertain nonlinearities are captured by the fuzzy systems that have been proven to be universal approximators. The proposed control scheme completely overcomes the singularity problem that occurs in the indirect adaptive feedback linearizing control. Projection in the estimated parameters and switching in the control input are both not required. The stability of the closed-loop system is guaranteed in the Lyapunov viewpoint.

• 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.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.108-114
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• 2008

Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.

• 전자공학회논문지SC
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• 제41권4호
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• pp.1-12
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• 2004

본 논문에서는 적응 퍼지 백스테핑 알고리즘을 이용하여 단일축 유연관절 로봇을 제어하는 새로운 알고리즘을 제안한다. 퍼지시스템은 일반근사기로 사용하여 로봇과 제어기의 비선형성과 불확실성을 상쇄하는 역할을 한다. 제안한 알고리즘은 추가적인 교시 제어기를 필요로 하지 않으며 추적오차를 상시유계시키는 특성이 있다. 끝으로 컴퓨터 모의실험을 통하여 제안한 방식의 성능을 확인한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.675-678
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• 1997

In this research we continue the study of nuclear power plant steam generator's intelligent modeling. This model represents the input-output behavior and is a preliminary stage for intelligent control. Among many intelligent models available, we study neural network models that have been proven as universal function approximators. We select multilayer perceptrons, circular backpropagation networks, piecewise linearly trained networks and recurrent neural networks as the candidates for the steam generator's intelligent models. We take the input-output pairs from steam generator's reference model and train the neural network models. We validate trained neural network models as intelligent models of steam generator.