• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.553-559
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• 1998

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 goes 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.267-274
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• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권1호
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.200-208
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• 2014

This paper presents the control of an inverted pendulum system using intelligent algorithms, such as fuzzy logic and neural networks, for advanced control education. The swing up balancing control of the inverted pendulum system was performed using fuzzy logic. Because the switching time from swing to standing motion is important for successful balancing, the fuzzy control method was employed to regulate the energy associated with the angular velocity required for the pendulum to be in an upright position. When the inverted pendulum arrived within a range of angles found experimentally, the control was switched from fuzzy to proportional-integral-derivative control to balance the inverted pendulum. When the pendulum was balancing, a joystick was used to command the desired position for the pendulum to follow. Experimental results demonstrated the performance of the two intelligent control methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.500-503
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• 2005

대부분의 지능 알고리즘들은 환경이나 사용목적에 따라 항상 최적 성능을 보장하지는 못한다. 그러므로 다양한 알고리즘들을 구현한 후에 환경이나 사용 목적에 따라 최적인 알고리즘 또는 알고리즘의 조합을 선택하여 시스템을 구성할 수 있다면 유용할 것이다 본 논문에서는 지능형 Macro Core를 기반으로 한 시스템 재설정 및 진화를 위한 지능형 아키텍처를 제안한다. 제안한 아키텍처를 이용하면 새로운 알고리즘들의 추가와 이들을 조합하여 시스템을 구성하는 데에 드는 비용을 절약할 수 있으며, 표준화된 규격을 제시할 수 있다는 장점이 있다. 제안한 Macro Core 기반의 지능형 아키텍처에 맞추어 시스템을 구성해 보고 이를 실제 얼굴 추출 및 인식 시스템 구성에 적용하고자 한다.

• 한국지능시스템학회논문지
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• 제15권7호
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• pp.823-827
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• 2005

대부분의 지능 알고리즘들은 환경이나 사용목적에 따라 항상 최적 성능을 보장하지는 못한다. 그러므로 다양한 알고리즘들을 구현한 후에 환경이나 사용 목적에 따라 최적인 알고리즘 또는 알고리즘의 조합을 선택하여 시스템을 구성할 수 있다면 유용할 것이다. 본 논문에서는 지능형 Macro Core를 기반으로 한 시스템 재설정 및 진화를 위한 지능형 아키텍처를 제안한다. 제안한 아키텍처를 이용하면 새로운 알고리즘들의 추가와 이들을 조합하여 시스템을 구성하는 데에 드는 비용을 절약할 수 있으며, 표준화된 규격을 제시할 수 있다는 장점이 있다. 제안한 Macro Core 기반의 지능형 아키텍처에 맞추어 시스템을 구성해 보고 이를 실제 얼굴 추출 및 인식 시스템 구성에 적용하고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.707-712
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• 1998

This paper presents several softcomputing techniques such as neural networks, fuzzy logic and genetic algorithms : Neural networks as brain metaphor provide fundamental structure, fuzzy logic gives a possibility to utilize top-down knowledge from designer, and genetic algorithms as evolution metaphor determine several system parameters with the process of bottom up development. With these techniques, we develop a pattern recognizer which consists of multiple neural networks aggregated by fuzzy integral in which genetic algorithms determine the fuzzy density values. The experimental results with the problem of recognizing totally unconstrained handwritten numeral show that the performance of the proposed method is superior to that of conventional methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1074-1077
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• 1993

The objective of this paper is to develop a fuzzy logic based decision-making system to detect low current faults using multiple detection algorithms. This fuzzy system utilizes a fuzzy expert model which executes an operation without complicated mathematical models. This fuzzy system decides the performance weights of the detection algorithms. The weights and the turnouts of the detection algorithms discriminate faults from normal events. This system can also be a generic group decision-making tool for other areas of power system protection.

• International Journal of CAD/CAM
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• 제3권1_2호
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• pp.77-83
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• 2003

We propose two algorithms to generate (1) polygonal meshes and (2) developable surface patches far generalized cylinders defined by contours of discrete curves. To solve the contour blending problem of generalized cylinder, the presented algorithms have adopted the algorithm and related properties of LIDM (linear interpolation by direction map) that interpolate geometric shapes based on direction map merging and group scaling operations. Proposed methods are fast to compute and easy to implement.