• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.4
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• pp.17-26
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• 1997

A enw channel current model of GaAs MeSFET suitagle for applications to microwave CAD has been developed. The current model includes the bias-dependent frequency dispersion effects and its parameters are extracted from the pulsed I-V measurements at several quiescent bias points. The model is verified by applying to the nonlinear circuit designs of power amplifier and MMIC mixer.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.368-373
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• 1998

This paper presents a GA-based fuzzy modelling scheme of nonlinear systems. The fuzzy model is a type of the Sugeno-Tagaki's fuzzy model whose consequence parts are described by a linear continuous dynamic equation as subsystem of a nonlinear system. The centers and width of the membership functions of the fuzzy sets defined over the input space and the orders and parameters of subsystems in the consequence parts are adjusted by a genetic algorithm. The effectiveness of the proposed method is verified

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.1-6
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• 1993

This report proposes a novel method of identification of continuous time-delay systems from sampled input-output data. By the aid of a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of th sampling period. Then an identification method combining the common linear least squares(LS) method or the instrumental variable(IV) method with the genetic algorithm(GA) is proposed. That is, the time-delay is selected by the GA, and the system parameters are estimated by the LS or IV method. Furthermore, the proposed method is extended to the case of multi-input multi-output systems where the time-delays in the individual input channels may differ each other. Simulation resutls show that our method yields consistent estimates even in the presence of high measurement noises.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.116-124
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• 1999

In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1949-1954
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• 2005

One of control methods for underactuated manipulators is known as a switching control which selects a partially-stable controller using a prespecified switching rule. A switching computed torque control with a fuzzy energy region method was proposed. In this approach, some partly stable controllers are designed by the computed torque method, and a switching rule is based on fuzzy energy regions. Design parameters related to boundary curves of fuzzy energy regions are optimized offline by a genetic algorithm (GA). In this paper, we discuss on parameters obtained by GA. The effectiveness of the switching fuzzy energy method is demonstrated with some simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.109.6-109
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• 2002

In this paper, the scheme of fuzzy system construction using GA(genetic algorithm) and FCM(Fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. in the structure identification, input data is trans-formed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, the number of fuzzy rule is obtained by a given performance criterion. In the parameter identification, the premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this, one can systematically obtain optimal parameter and the v..

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1065-1070
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• 2003

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.142-148
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• 2008

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• Journal of the Optical Society of Korea
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• v.10 no.2
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• pp.86-90
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• 2006

The external feedback effect on the relative intensity noise (RIN) characteristics of blue InAlGaN laser diode has been analyzed taking into account the spontaneous emission noise and the injection current for the high frequency modulation. A Langevin diffusion model was exploited to characterize its relative intensity noise. The simulation parameters were quantitatively evaluated from the optical gain properties of the InAlGaN multiple quantum well active regions by using the multiband Hamiltonian for the strained wurtzite crystals. The extracted parameters were then applied to the rate equations taking into account the external feedback and the high frequency modulation current. The RIN characteristics were investigated to optimize the low frequency laser diode noise characteristics.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.520-525
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• 2003

The determination of unknown parameters in rotating machinery is a difficult task and optimization techniques represent an alternative technique for parameter identification. The Simulated Annealing(SA) and Genetic Algorithm(GA) are powerful global optimization algorithm. This paper proposes new hybrid algorithm which combined GA with SA and local search algorithm for the purpose of parameter identification. Numerical examples are also presented to verify the efficiency of proposed algorithm. And, this paper presents the general methodology based on hybrid algorithm to identify unknown bearing parameters of flexible rotors using measured unbalance responses. Numerical examples are used to ilustrate the methodology used, which is then validated experimentally.