• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.1703_1704
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• 2009

Fuzzy neural ship controllers is used in ship steering control. It can make full use of the advantage of all kinds of intelligent algorithms. This provides an efficient way for this paper. An RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network can be decreased by using union-rule configuration in the hidden layer of the network. The ship control quality is effectively improved in case of appending additional sea state disturbance. The performance of controller is evaluated by the system simulation using simulink tools.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.671-672
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• 2008

Hybrid intelligent technique is used in ship steering control. It can make full use of the advantage of all kinds of intelligent algorithms. This provides an efficient way for this paper. An RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network can be decreased by using union-rule configuration in the hidden layer of the network. The ship control quality is effectively improved in case of appending additional sea state disturbance. The performance of controller is evaluated by the system simulation using Matlab.

• 한국해양공학회지
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• 제21권6호
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• pp.115-119
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• 2007

Inference rules of the knowledge base, generated by experts or optimization, may be often inconsistent and incomplete. This paper suggests a systematic and automatic method which extracts inference rules not from experts' subject but from data. First, input/output linguistic variables are partitioned into several properties by the fuzzy equalization algorithm and each combination of their properties comes to premise of inference rule. Then, the conclusion which is the mast suitable for the premise is selected by evaluating consistent measure. This method, automatically from data, derives inference rules from experience. It is shown through application that extracts new inference rules between hull dimensions and hull performance.

• 전자공학회논문지B
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• 제29B권2호
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• pp.60-68
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• 1992

Fuzzy logic rule based controller has many desirable advantages, whih are simple to implement on the real time and need not the information of structure and dynamic characteristics of the system. Thus, nowadays, the scope of the application of the fuzzy logic controller becomes enlarged. But, if the controlled plant is a time-varying/nonlinear system, it is not easy to construct the fuzzy logic rules which need the knowledge of and expert. In this paper, an approach by which the logic control rules can be auto-generated using the genetic algorithm that is known to be very effective in the optimization problem will be proposed and the effectiveness of the proposed approach will be verified by computer simulation of the 2 d.o.f. planner robot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.177-180
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• 1996

Fuzzy control systems depend on a number of parameters such as the shape or magnitude of the fuzzy membership functions, etc. Conventional fuzzy reasoning method can not be easily applied to the multi-input multi-output(MIMO) system due to the large number of rules in the rule base. Recently Z. Cao et al have proposed a New Fuzzy Reasoning Method(NFRM) which turned out to be superior to Zadeh's FRM. We have extended the NFRM to handle the MIMO system. However, it is difficult to choose a proper relation matrix of the NFRM. Therefore, we have modified the evolution strategy(ES), which is one of the optimization algorithms, to do efficiently the tuning operation for the extended NFRM. Finally we applied the extended NFRM with the modified ES to tracking control of robot manipulator.

• 한국정보통신학회논문지
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• 제5권7호
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• pp.1374-1383
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• 2001

기존의 제어방식 보다 강인성이 우수한 퍼지 논리 제어방식에서 최적화되지 않은 제어규칙을 이용하여, 오프라인 상에서 소속함수 관계와 스케일링 팩터를 유전자알고리즘으로 최적화한 후, 온라인으로 퍼지제어기를 구성하는 제어방식을 제안하였다. 제안한 방식을 단일 링크 매니률레이터의 추종제어에 적용하여 기존 퍼지제어 방식과 비교 검토한 결과 퍼지제어규칙의 수도 감소하고 제어성능도 우수함을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1228-1229
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• 2011

A novel approach has been promoted for FNN ship controllers. An Electro-hydraulic governor has been widely adopted to the ship speed control of propulsion marine diesel engines for a long time, it was very difficult for Electro-hydraulic governor to regulate the speed of high power engine with long stroke at low speed and low load, because of the jiggling phenomena by rough fluctuation of rotating torque and the hunting phenomena by long dead time occurred in fuel combustion process in the engine cylinder. This paper provides an efficient way for improving control performance by FNN controller. An RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network can be decreased by using union-rule configuration in the hidden layer of the network. The performance of controller is evaluated by the system simulation using simulink tools.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.74-76
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• 2009

In this paper, search method and sliding mode observer are developed for efficiency optimization of induction motor. The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. The search controller is based on the "Rosenbrock" method and finds the flux level at the minimum input power of induction motor. Once this optimal flux level has been determined, this information is utilized to update the rule base of a fuzzy controller A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is also used to compensate for mechanical uncertainties in the speed control of induction motor. Simulation results are presented to validate the proposed controller.

• 전자공학회논문지C
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• 제35C권12호
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• pp.85-98
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• 1998

본 논문은 특정 응용에 적합한 퍼지 제어기의 최적 설계 파라메터 (퍼지 규칙과 소속 함수)를 찾는데 역전파 학습 과정과 유전 알고리즘을 결합한 Lamarckian 상호적응 기법을 이용한 뉴로-퍼지 제어기의 새로운 설계 방법을 제안한다. 설계 파라메타들은 진화에 의한 전역적 탐색을 통해 높은 포함값과 유용한 퍼지 규칙들을 갖는 규칙 베이스와 작은 근사화 오차와 좋은 제어 성능을 갖는 소속 함수들을 얻도록 제어기간 파라메타 조절을 수행하며, 학습에 의한 국부적 탐색을 통해 각 퍼지 제어기가 원하는 제어 결과를 나타내도록 제어기내 파라메타 조절을 수행한다. 제안한 상호적응 설계 방법은 유전 알고리즘의 모든 세대에서 역전파 학습이 이루어지므로 보다 좋은 근사화 능력을 나타나고, 사용한 무게 중심 비퍼지화기가 정확한 비퍼지화값을 계산하므로 보다 좋은 제어 성능을 가지며, 퍼지 규칙 베이스와 소속 함수들의 최적화 탐색 과정이 입출력 공간의 같은 퍼지 분할 상에서 통합된 적응 함수에 의하여 동시에 수행되므로 탐색을 위한 작업 공간이 아주 작아지는 장점이 있다. 시뮬레이션 결과는 Lamarckian 상호 적응에 의해 얻어진 FLC가 퍼지 규칙수, 근사화 능력, 제어 성능등 모든 면에서 다른 방법에 의해 얻어진 FLC보다 가장 우수함을 보여준다.