• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.39.2-39
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• 2001

This paper describes the design of an output-feedback controller based on an adaptive fuzzy observer for uncertain single-input single-output nonlinear dynamical systems. Especially, we have focused on the realization of minimal dynamic order of the adaptive fuzzy observer. For the purpose, we propose a new method in which no strictly positive real(SPR) condition is needed and combine dynamic rule activation scheme with on-line estimation of fuzzy parameters. By using proposed scheme, we can reduce computation time, storage space, and dynamic order of the adaptive fuzzy observer ...

• Food Science and Biotechnology
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• 제15권6호
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• pp.871-876
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• 2006

Rheological behavior of commercial hot pepper-soybean paste (HPSP) was evaluated in small amplitude oscillatory and steady shear tests. Storage modulus (G'), loss modulus (G"), and complex viscosity (${\eta}^*$) as a function of angular frequency (${\omega}$), and shear stress (${\sigma}$) as a function of shear rate (${\gamma}$) data were obtained for 5 commercial HPSP samples. HPSP samples at $25^{\circ}C$ exhibited a non-Newtonian, shear-thinning flow behavior with high yield stresses and their flow behaviors were described by power law, Casson, and Herschel-Bulkley models. Time-dependent flow properties were also described by the Weltman, Hahn, and Figoni & Shoemaker models. Apparent viscosity over the temperature range of $5-35^{\circ}C$ obeyed the Arrhenius temperature relationship with activation energies (Ea) ranging 18.3-20.1 kJ/mol. Magnitudes of G' and G" increased with an increase in ${\omega}$, while ${\eta}^*$ decreased. G' values were higher than G" over the most of the frequency range (0.63-63 rad/sec), showing that they were frequency dependent. Steady shear viscosity and complex viscosity of the commercial HPSP did not fit the Cox-Merz rule.

• 제어로봇시스템학회논문지
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• 제7권8호
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• pp.664-674
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• 2001

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.

• 한국도로학회논문집
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• 제7권1호
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• pp.49-61
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• 2005

아스팔트 혼합물의 동탄성계수는 시험온도 하중주파수의 조합에 따라 각각의 동탄성계수값을 평가한다. 실험에서 얻어진 각각의 동탄성계수를 하중시간과 온도중첩원리를 이용하여 마스터곡선(Master Curve)을 결정한다. 본 연구의 주목적은 마스터곡선을 만들기 위해 필요한 3개의 다른 전이함수(Shift Factor)에 -즉, Arrhenius, 2002 AASHTO Guide, Experimental method- 따른 마스터곡선의 변화정도를 평가하는 것이다. 평가를 위해 사용된 골재는 화강암이고, 아스팔트(AP-3 및 AP-5)를 이용하여 표층용 및 기층용 아스팔트 혼합물의 동탄성계수를 평가하였다. 배합설계는 Superpave Level 1 기준을 준용하였고, 다짐은 선회다짐기를 이용하였다. UTM시험기를 이용한 동탄성계수 시험은 5개의 온도(-10, 5, 20, 40, 55도) 및 5개의 하중주파수(0.05, 0.1, 1, 10, 25 Hz)를 이용하였고, 각각의 아스팔트 혼합물의 위상각 및 동탄성계수를 평가하였다. 측정된 값을 이용하여 Sigmoidal Function방정식을 만족하는 입력변수를 결정하기 위해 전이함수 및 활성에너지 (activation energy)를 결정하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권3호
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.

• 전기전자학회논문지
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• 제7권2호
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• pp.156-165
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• 2003

본 논문은 불확실한 비선형 계통에 대해서 강인한 적응 퍼지 관측기를 설계하는 방법을 제시한다. 새로 제시하는 관측기는 관측기 설계시 관측오차의 동특성식이 SPR (strictly positive real)이어야 한다는 조건이 불필요하다. 또한 불확실한 항에 대한 유계상수도 추정하는 알고리듬을 사용하여 강인항의 이득값을 설계자가 미리 결정할 필요가 없게 된다. 설계된 관측기를 포함한 전체 폐루프 계통에 대해서 리아프노브 안정도를 증명하였으며 관측오차를 포함한 계통의 모든 신호들의 반전역적 유계(semi-global uniform ultimate boundedness)임을 증명하였다. 이론적으로 도출된 결과를 mass-spring-damper 계통에 대한 모의실험을 수행하여 제안된 관측기의 효율성과 성능을 보였다.