• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.43-46
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• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.1-5
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• 2001

In this paper, we propose a fuzzy traffic controller that is able to cope with traffic congestion appropriately. In order to consider such situation as loss of green time caused by spillback of upper crossroad, it imports a degree of traffic congestion of upper roads which vehicles on a crossroad are to proceed to. We constructed the equal-partitioned fuzzy traffic controller that uses the membership functions of the same size and shape, and modified the size and shape, and modified the size and shape of its membership functions by the membership function modification algorithm. In experiment, we compared and analyzed the fixed signal controller, the fuzzy traffic controller with the membership of the same size and shape, and the modified fuzzy traffic controller by using the delay time, the proportion of entered vehicles to occurred vehicles and the proportion of passed vehicles to entered vehicles. As a result of experiment, the modified fuzzy controller showed more enhanced performance than others.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• 정보처리학회논문지B
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• 제8B권2호
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• pp.123-129
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• 2001

본 연구에서는 전문가와 운전자의 제어 지식을 더 정확하게 표현하여 퍼지 논리 제어기의 성능을 향상시킬 수 있는 소속함수 수정 알고리즘을 제안한다. 제안된 알고리즘은 제어지식을 더 정확히 표현할 수 있도록 직관적인 지식과 경험으로부터 유추된 대략적인 제어지식을 평가기준으로 하고 입출력 데이터 클러스터링에 의해 소속함수의 형태와 위치를 수정한다. 제안된 방법을 수위 조절 모델과 교통신호 제어 모델에 적용한 실험을 통해서, 제안된 알고리즘이 기존 제어기의 성능을 향상시킬 수 있고, 퍼지 제어기에서 언어적 변수에 대한 구간 설정의 어려움을 해결할 수 있음을 알 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.120.2-120
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• 2002

In this paper auto-tuning scheme of weight function in the neural networks has been suggested by immune algorithm for nonlinear process. A number of structures of the neural networks are considered as learning methods for control system. A general view is provided that they are the special cases of either the membership functions or the modification of network structure in the neural networks. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also. It can provi..

• 한국지능시스템학회논문지
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• 제11권8호
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• pp.720-725
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• 2001

비선형 미분방정식으로부터 TSK(Takagi-Sugeno-Kang) 퍼지모델을 유도한느 것은 퍼지 제어의 이론분야에서는 매우 중요한 문제이다. 본 논문에서는 off-equilibrium에서 상수항을 가지는 부분 미분 방정식을 배제시키는 방법을 제안한다. 이는 전건부의 언어적 표현이 삼각형 소속함수들을 가지는 기본적인 TSK 퍼지모델에서 체계적으로 유도되어진다. 그리고, 유도된 TSK 퍼지모델의 전건부 소속함수들은 GA(Genetic Algorithm)를 이용하여 최적화함으로써 실제 미분방적식에 근사화한다. 아울러 이상의 제안된 방법의 우수성을 모의실험을 통하여 검증한다.

• 한국지능시스템학회논문지
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• 제13권6호
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• pp.722-728
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• 2003

In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

• 대한음성학회지:말소리
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• 제64호
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• pp.121-135
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• 2007

Speech signal is distorted by channel characteristics or additive noise and then the performances of speaker or speech recognition are severely degraded. To cope with the noise problem, we propose a modified HMM decoder algorithm using SNR-based observation confidence, which was successfully applied for GMM in speaker identification task. The modification is done by weighting observation probabilities with reliability values obtained from SNR. Also, we apply PSO (particle swarm optimization) method to the confidence function for maximizing the speaker identification performance. To evaluate our proposed method, we used the ETRI database for speaker recognition. The experimental results showed that the performance was definitely enhanced with the modified HMM decoder algorithm.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2000년도 춘계학술대회 논문집(Proceeding of the KOSME 2000 Spring Annual Meeting)
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• pp.16-21
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• 2000

In this paper we design a GA-fuzzy controller for position control and anti-swing at the destination point. Applied genetic algorithm is used to complement the demerit such as the difficulty of the component selection of fuzzy controller namely scaling factor membership function and control rules. lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling. Simulation results show that the proposed control technique is superior to a conventional optimal control in destination point moving and modification.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.238-242
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• 1991

This paper presents an approach to self-tuning PI control of dynamic plants, based on fuzzy logic application. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a fuzzy logic controller, one of the most difficult problem is the selection of linguistic control rules and parameters. To overcome this difficulty, self-tuning fuzzy PI controller (STFPIC) with a hierarchical structure in which the fuzzy PI controller is assigned as the lower level and the rule modification and parameter adjustment as the higher level. The rules and parameters are generated by the adjustment of membership function through performance index(PE). In this paper, the algorithm for of the controller performance is estimated by means of computer simulation.