• Title, Summary, Keyword: pseudo convexity

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An Integration Type Adaptive Compensator for a Class of Linearly Parameterized Systems (선형 파라미터화된 시스템에 대한 적분형 적응보상기)

  • Yoo Byung-Kook;Yang Keun-Ho
    • Journal of the Institute of Convergence Signal Processing
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    • v.6 no.2
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    • pp.82-88
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    • 2005
  • A compensation scheme for a class of linearly parameterized systems is presented. The compensator consists of a typical linearizing control and an adaptive observer with integration type update law, which is based on Speed Gradient (SG) algorithm.. Instead of the intermediate functions of the compensation schemes suggested by other researchers, the proposed compensator is designed with some design functions which guarantee the growth, convexity, attainability, and pseudo gradient conditions in the update law. The scheme achieves the asymptotic stability of the tracking error and the boundedness of the estimation errors. A numerical example is given to demonstrate the validity of the proposed design.

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COMPLEX SCALING AND GEOMETRIC ANALYSIS OF SEVERAL VARIABLES

  • Kim, Kang-Tae;Krantz, Steven G.
    • Bulletin of the Korean Mathematical Society
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    • v.45 no.3
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    • pp.523-561
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    • 2008
  • The purpose of this paper is to survey the use of the important method of scaling in analysis, and particularly in complex analysis. Applications are given to the study of automorophism groups, to canonical kernels, to holomorphic invariants, and to analysis in infinite dimensions. Current research directions are described and future paths indicated.

ON $\varepsilon$-BIRKHOFF ORTHOGONALITY AND $\varepsilon$-NEAR BEST APPROXIMATION

  • Sharma, Meenu;Narang, T.D.
    • The Pure and Applied Mathematics
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    • v.8 no.2
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    • pp.153-162
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    • 2001
  • In this Paper, the notion of $\varepsilon$-Birkhoff orthogonality introduced by Dragomir [An. Univ. Timisoara Ser. Stiint. Mat. 29(1991), no. 1, 51-58] in normed linear spaces has been extended to metric linear spaces and a decomposition theorem has been proved. Some results of Kainen, Kurkova and Vogt [J. Approx. Theory 105 (2000), no. 2, 252-262] proved on e-near best approximation in normed linear spaces have also been extended to metric linear spaces. It is shown that if (X, d) is a convex metric linear space which is pseudo strictly convex and M a boundedly compact closed subset of X such that for each $\varepsilon$>0 there exists a continuous $\varepsilon$-near best approximation $\phi$ : X → M of X by M then M is a chebyshev set .

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A study of global minimization analaysis of Langevine competitive learning neural network based on constraction condition and its application to recognition for the handwritten numeral (축합조건의 분석을 통한 Langevine 경쟁 학습 신경회로망의 대역 최소화 근사 해석과 필기체 숫자 인식에 관한 연구)

  • 석진욱;조성원;최경삼
    • 제어로봇시스템학회:학술대회논문집
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    • pp.466-469
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    • 1996
  • In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.

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