• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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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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• v.15 no.6
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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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.

• International Journal of Highway Engineering
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• v.7 no.1 s.23
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• pp.49-61
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• 2005

The dynamic modulus of hot mix asphalt can be determined according to the different combinations of testing temperature and loading frequency. The superposition rule is adapted to get the master curve of dynamic modulus for each hot mix asphalt. There are couple of different methods to get the shift factor which is a key for making the master curve. In this paper, Arrehnius, 2002 AASHTO, and experimental method was employed to get the master curve. Evaluation of dynamic modulus for 25mm base course of hot mix asphalt with granite aggregate and two asphalt binders(AP-3 and AP-5) was carried out. Superpave Level 1 Mix Design with gyratory compactor was adopted to determine the optimum asphalt binder content(OAC) and the measured ranges of OAC were between 4.1% and 4.4%. UTM was used for laboratory test. The dynamic modulus and phase angle were determined by testing on UTM, with 5 different testing temperature(-10, 5, 20, 40, & $55^{\circ}C$) and 5 different loading frequencies(0.05, 0.1, 1, 10, 25 Hz). Using the measured dynamic modulus and phase angle, the input parameters of Sigmoidal function equation to represent the master curve were determined and these will be adopted in FEM analysis for asphalt pavements. The shift factor and activation energy for determination of master curve were calculated.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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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.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.156-165
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• 2003

This paper describes the design of a robust adaptive fuzzy observer for uncertain nonlinear dynamical system. We propose a new method in which no strictly positive real (SPR) condition is needed. No a priori knowledge of an upper bound on the lumped uncertainty is required. The Lyapunov synthesis approach is used to guarantee a semi-global uniform ultimate boundedness property of the state observation error, as well as of all other signals in the closed-loop system. The theoretical results are illustrated through a simulation example of a mass-spring-damper system.