• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.461-464
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• 2007

In this paper, we propose a fuzzy Kalman filtering to deal with a estimation error covariance. The T-S fuzzy model structure is further rearranged to give a set of linear model using standard Kalman filter theory. And then, to minimize the estimation error covariance, which is inferred using the fuzzy system. It can be used to find the exact Kalman gain. We utilize the genetic algorithm for optimizing fuzzy system. The proposed state estimator is demonstrated on a truck-trailer.

• 한국전자파학회논문지
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• 제14권5호
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• pp.499-505
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• 2003

이동 무선 통신 시스템을 설계할 때 기지국의 위치는 매우 중요한 파라미터 중 하나이다. 기지국 위치를 설계할 때 여러 가지 복잡한 변수들을 잘 조합하여 코스트가 최소가 되도록 설계해야 한다. 이러한 문제를 해결하는데 필요한 알고리즘이 조합 최적화 알고리즘이며, 지금까지 조합 최적화 기술로 Random Walk, Simulated Annealing, Tabu Search, Genetic Algorithm과 같은 전역 최적화 기술이 사용되어 왔다. 본 논문은 이동 통신시스템의 기지국 위치 최적화에 위의 4가지 알고리즘들을 적용하여 각 알고리즘의 결과를 비교 분석하며 알고리즘에 의한 최적화 과정을 보여준다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제3권1호
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• pp.115-125
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• 품질경영학회지
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• 제36권2호
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• pp.9-19
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• 2008

Spare part problem of MIME (Multi Indenture Multi Echelon) system under availability constraint has been studied for several decades. In most of existing studies, it was very difficult to obtain the optimal numbers of spare parts and some approximate methods were proposed under many restrictions. In this paper, we consider a simulation to estimate the total cost rate and system availability and a genetic algorithm to obtain the optimal numbers of spare parts. Some numerical examples are also studied.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.50-56
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• 2000

This paper presents the design of an optimal state feedback controller for container cranes under some design specifications. To do this, the nonlinear equation of a container crane system is linearized and then augmented to eliminate the steady-state error, and some constraints are derived from the design specifications. Designing the controller involves a constrained optimization problem which classical gradient-based methods have difficulties in handling. Therefore, a real-coding genetic algorithm incorporating the penalty strategy is used. The responses of the proposed control system are compared with those of the unconstrained optimal control system to illustrate the efficiency.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2000년도 춘계학술대회 논문집(Proceeding of the KOSME 2000 Spring Annual Meeting)
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• pp.6-10
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• 2000

In this paper a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a low-pass prefilter the inverse dynamic model of a system and the PI controller. The low-pass prefilter prevents high frequency effects from the inverse dynamic model. The model is characterized by a nonlinear friction model. The PI controller regulates the error between the set-point and the system output which is caused by modeling error disturbances and variations f parameters. The parameters of the model and the PI controller are optimized offlinely by genetic algorithm. The experimental results on a DC motor system illustrate the performance of the proposed controller.

• 대한전기학회논문지:전력기술부문A
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• 제54권2호
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• pp.51-60
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• 2005

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented by adding a fuzzy precompensator to an existing TCSC-PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Nonlinear simulation results show that the proposed control technique is superior to conventional TCSC-PSS in dynamic responses over the wide range of operating conditions and in convinced robust and reliable in view of structure.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 전문대학교육위원 P
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• pp.43-51
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• 1999

The power flow calculations(PFc) are the most important and powerful tools in power systems engineering. The conventional power flow problem is solved generally with numerical methods such as Newton-Raphson(NR). The conventional numerical method generally have some convergency problem, which is sensitive to initial value, and numerical stability problem concerned with jacobian matrix inversion. This paper presents a new PFc algorithm based on the improved genetic algorithm (IGA) which can overcome the disadvantages mentioned above. The parameters of GA, with dynamical hierarchy of the coding system, are improved to make GA a practical algorithm in the problem of real system. Some case studies with test bus system also present to show the performance of proposed algorithm. The results of proposed algorithm are compared with the results of PFc obtained using a conventional NR method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1086-1091
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• 2005

This paper focuses on intelligent control of induction motor by hybrid system consisting of GA-PSO. Induction motor has been using in industrial area. However, it is challengeable on how we control effectively. From this point, an optimal solution using GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is introduced to intelligent control. In this case, it is possible to obtain local solution because chromosomes or individuals which have only a close affinity can convergent. To improve an optimal learning solution of control, This paper deal with applying PSO and Euclidian data distance to mutation procedure on GA's differentiation. Through this approaches, we can have global and local optimal solution together, and the faster and the exact optimal solution without any local solution. Four test functions are used for proof of this suggested algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.