• 한국지능시스템학회논문지
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• 제18권5호
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• pp.642-648
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• 2008

여자유도 매니퓰레이터의 동적 제어는 관절에 가해지는 토크를 최소화하는 목적으로 많은 연구가 이루어져 왔다. 그러나 기존의 국소 토크 최적화의 동적 제어 방법은 드라이버로 구현하기 힘든 토크가 요구된다. 본 논문에서는 그러한 큰 토크 요구를 상당히 개선시킨 새로운 제어 알고리즘을 제안한다. 이 알고리즘은 기존의 국소 토크 최소화 알고리즘에 퍼지 로직과 유전자 알고리즘을 적용시킨 것이다. 제안된 알고리즘은 3자유도 평면 여자유도 로봇에 적용하였으며, 시뮬레이션 결과를 통하여 제안된 알고리즘의 타당성을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.327-332
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• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• 한국지능시스템학회논문지
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• 제6권2호
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• pp.43-51
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• 1996

퍼지제어에서 퍼지규칙은 퍼지제어기의 제어결정을 내리는데 중요한 역할을 한다. 그래서, 제어성능은 주로 퍼지규칙의 질에 의해서 결정된다. 본 논문에서 우리는 교차와 돌연번이의 확률이 적응적으로 변화되는 유전자 알고리즘을 사용하여 퍼지규칙을 최적화 하는 방법을 기술한다. 또한 본 논문에서 우리는 플랜트의 응답을 듀개의 부분으로 나누어 제어 목적을 만족하게 하는 적합도 측정 방식을 제안한다. 좀더 빠른 해답을 얻기 위해 우리는 초기의 퍼지규칙으로 무작위적인 규칙을 사용하지 않고 자동으로 퍼지규칙을 생성하는 방법을 사용하여 초기 퍼지규칙으로 사용했다. 이렇게 얻어진 퍼지규칙이 좋은 것인지를 보여주기 위해 비선형 플랜트를 이용하여 시뮬레이션 해보았다. 시뮬레이션 결과 우리의 방법이 합리적이고 유용한 것임이 밝혀졌다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.301-304
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• 2005

In this paper, we propose a new fuzzy set-based polynomial neuron (FSPN) involving the information granule, and new fuzzy-neural networks - Fuzzy Set based Polynomial Neural Networks (FSPNN). We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. We have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2469-2490
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• 2019

A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT's Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs).

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.215-222
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• 2001

신경망은 선형 시스템뿐만 아니라 비선형 시스템에 있어서도 탁월한 모델링 및 예측 성능을 갖고 있다. 하지만 좋은 성능을 갖는 신경망을 구현하기 위해서는 최적화 해야할 파라미터들이 있다. 은닉층의 뉴런의 수, 학습율, 모멘텀, 학습오차 등이 그것인데 이러한 파라미터들은 경험에 의해서, 또는 문헌들에서 제시하는 값들을 선택하여 사용하는 것이 일반적인 경향이다. 하지만 신경망의 전체적인 성능은 이러한 파라미터들의 값에 의해서 결정되기 때문에 이 값들의 선택은 보다 체계적인 방법을 사용하여 구하여야 한다. 본 논문은 유전 알고리즘을 이용하여 이러한 신경망 파라미터들의 최적 값을 찾는데 목적이 있다. 유전 알고리즘을 이용하여 찾은 파라미터들을 사용하여 학습된 신경망의 학습오차와 예측오차들을 심플렉스 알고리즘을 이용하여 찾는 파라미터들을 사용하여 학습된 신경망의 오차들과 비교하여 본 결과 유전 알고리즘을 이용하여 찾을 파라미터들을 이용했을 때의 신경망의 성능이 더욱 우수함을 알 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.139-139
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• 2000

The subject of this paper is the tole operation for unmanned vehicle. The aim is studied in context of motor control system and algorithms for the mid to low level control of tele operation unmanned vehicle described. Modern, vehicle related researches have been implemented about control, chassis, body and safe쇼 but now is to driving comfort, I.T.S. and human factor, etc. As a result of this fact, unmanned vehicle is main research topic over the world but it is still very expensive and unreasonable. A hierarchical approach is studied in context of motor control system and algorithms for the mid to low level control of tele operation unmanned vehicle described. The real time control and monitoring of longitudinal, lateral, Pitching motion is to be solved by system integration and optimization technique. We show the experimental result about fixed brake range test and acceleration test. And all system is to integrated for driving simulator and unmanned vehicle.

• Journal of Communications and Networks
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• 제14권6호
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• pp.672-681
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• 2012

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.358-363
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• 2003

Optimal tuning plays an important role in operations or tuning of the complex process such as the main steam temperature of the thermal power plant. However, it is very difficult to maintain the steam temperature of power plant using conventional optimization methods, since these processes have the time delay and the change of the dynamic characteristics in the reheater. Up to the present time, the Pm controller has been used. However, it is not easy to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper suggests immune algorithm based tuning technique for PID Controller on steam temperature process with long dead time and its results are compared with genetic algorithm based tuning technique.