• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.977-984
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• 2001

In this paper, Genetic-Fuzzy Algorithm for Inverted Pendulum is presented. This Algorithms is combine Fuzzy logic with the Genetic Algorithm. The Fuzzy Logic Controller is only designed to two inputs and one output. After Fuzzy control rules are determined, Genetic Algorithm is applied to tune the membership functions of these rules. To measure of performance of the designed Genetic-Fuzzy controller, Computer simulation is applied to Inverted Pendulum system. In the simulation, In the case of f[0.3, 0.3] Fuzzy controller is measured that maximum undershoot is $-5.0 \times 10^{-2}[rad]$, maximum undershoot is $3.92\times10^{-2}[rad]$ individually however, Designed algorithm is zero. The Steady state time is approximated that Fuzzy controller is 2.12[sec] and designed algorithm is 1.32[sec]. The result of simulation, Resigned algorithm is showed it's efficient and effectiveness for Inverted Pendulum system.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.759-764
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• 2004

본 연구는 Deng(1989)이 제시한 Grey 모형을 이용하여 성진강댐의 월유입량을 예측하였고 그 방법을 제시하였다. Grey 모형은 시계열모형이나 다른 모형에 비해 비교적 적은 수의 자료를 이용하고, 간단할 수식으로 구성되어 있는 장점이 있으나, 적은 수의 자료로 인해 입력자료가 가지는 증감의 경향(trend)으로 오차가 발생하기 쉽다. 그러므로 예측오차를 극복하기 위해서 Fuzzy 시스템을 결합한 Fuzzy-Grey 모형을 구성하였고 Fuzzy 시스템에 필요한 매개변수를 추정하기 위해 최적화기법인 유전자 알고리즘(GA; Genetic Algorithm)을 이용하였다. Grey 모형과 결합된 Fuzzy 시스템은 현재의 입력자료가 가지는 패턴과 가장 유사한 패턴의 과거자료를 이용하여 현재의 입력자료의 예측오차를 추론해내는 기능을 가진다. 오차를 추론하기 위해서 과거 월유입량 자료중 현재 입력 자료와 유사한 패턴을 Grey 상관도를 이용하여 검색하고, 보다 높은 유사성을 가지는 패턴을 선별하고자 노름(norm)을 사용하였고, 유전자 알고리즘의 탐색공간을 제한하였다. 이렇게 구성한 Fuzzy-Grey 모형을 이용하여 전국적인 가뭄년도였던 1992년, 1988년, 2001년에 대해 섬진강댐의 월유입량을 예측하였다. 오차는 1982년, 2001년, 1988년 순으로 비슷한 크기의 오차가 발생하였는데 결과를 분석하여 보면, 급격한 월유입량의 변화가 있었던 경우에 오차가 크게 발생하였으나 가뭄년도에 대해 월유입량의 불확실성이 큼에도 불구하고 비교적 월유입량의 추세를 잘 예측한 것으로 판단된다. 본 연구에서 적용한 Fuzzy-Grey 모형은 적은 수의 자료를 이용하여 예측하고 예측결과를 다시 입력자료로 사용하는 업데이트 방식을 사용하기 때문에 예측결과의 오차가 완전하게 보정되지 않으면 다음 결과에 역시 오차를 주게 되어 오차보정이 상당히 중요하다는 것을 알 수 있었다. 오차를 보다 효과적으로 보정하기 위해서는 퍼지제어에 사용되는 퍼지규칙의 수를 늘리고, 유입량에 직접적인 영향을 주는 강우량과 연계한 2변수의 Fuzzy-Grey 모형을 이용한다면 보다 정확한 유입량 예측이 가능할 것으로 사료된다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1534-1538
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• 2005

In this paper, we propose a method to detect contours of a face, eyes, and a mouth of a person in the color image in order to make an avatar automatically. First, we use the HSI color model to exclude the effect of various light conditions, and find skin regions in the input image by using the skin color defined on HS-plane. And then, we use deformable templates and genetic algorithm (GA) to detect contours of a face, eyes, and a mouth. Deformable templates consist of B-spline curves and control point vectors. Those represent various shapes of a face, eyes and a mouth. GA is a very useful search algorithm based on the principals of natural selection and genetics. Second, the avatar is automatically created by using GA-detected contours and Fuzzy C-Means clustering (FCM). FCM is used to reduce the number of face colors. In result, we could create avatars which look like handmade caricatures representing user's identity. Our approach differs from those generated by existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• Advances in concrete construction
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• v.11 no.1
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.115-121
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• 2002

This paper presents an evolutionary design of digital IIR filters using the genetic algorithm (GA) with modified genetic operators and real-valued encoding. Conventional digital IIR filter design methods involve algebraic transformations of the transfer function of an analog low-pass filter (LPF) that satisfies prescribed filter specifications. Other types of frequency-selective digital fillers as high-pass (HPF), band-pass (BPF), and band-stop (BSF) filters are obtained by appropriate transformations of a prototype low-pass filter. In the GA-based digital IIR filter design scheme, filter coefficients are represented as a set of real-valued genes in a chromosome. Each chromosome represents the structure and weights of an individual filter. GA directly finds the coefficients of the desired filter transfer function through genetic search fur given filter specifications of minimum filter order. Crossover and mutation operators are selected to ensure the stability of resulting IIR filters. Other types of filters can be found independently from the filter specifications, not from algebraic transformations.

• Journal of Mechanical Science and Technology
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• v.17 no.2
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• pp.187-196
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• 2003

In this paper, a new methodology of evolutionary computations - An Adaptive Evolutionary Algorithm (AEA) is proposed. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations : global search capability of GA and local search capability of ES. In the reproduction procedure, the proportions of the population by GA and ES are adaptively modulated according to the fitness. AEA is used to. designing fuzzy logic controller (FLC) for a high-angle-of-attack flight system for a super-maneuverable version of F-18 aircraft. AEA is used to determine the membership functions and scaling factors of an FLC. The computer simulation results show that the FLC has met both robustness and performance requirements.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.187-190
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• 2002

주어진 시스템의 정확한 제어를 위해 뉴로-퍼지 제어시스템의 성공적인 제어는 그 네트웍의 구성에 크게 의존한다. 현재 유전알고리즘을 사용한 제어기 구조의 최적화 방법에 대한 많은 연구가 이루어지고 있으나, 기존의 유전 알고리즘은 고정된 길이의 스트링 구조로 인하여 적합한 연계(linkage)를 얻기 어렵다는 단점이 있다 본 논문에서는 뉴로-퍼지 제어기의 구조적 최적화 설계의 새로운 방법을 제안한다. 여기서, 우리는 구조적으로 최적화 된 뉴로-퍼지 제어기를 설계하기 위해 가변길이 스트링을 사용하는 메시 유전 알고리즘(messy Genetic Algorithm mGA)을 사용한다. 그리고 제안된 방법의 우수성을 증명하기 위해 대표적인 비선형 시스템인 cart-pole 시스템에 제안된 방법을 적용한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.171-175
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• 2007

In this paper a methodology has been proposed to compute the parameters of the multilayer earth model using a genetic algorithm(GA). The results provided by the GA constitute the indispensable data that can be used in circuital or field simulations of grounding systems. This methodology allows to proceed toward a very efficient simulation of the grounding system and an accurate calculation of potential on the ground's surface. The sets of soil resistivity used for GA are measured in Jeju area.