• 산업경영시스템학회지
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• 제31권4호
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• pp.140-145
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• 2008

Fuzzy partition is a conceptual vehicle that encapsulates data into information granules. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. A few algorithms generating fuzzy partitions with fuzzy equalization have been suggested. Simulations and experiments have showed that fuzzy partition representing more characteristics of given input distribution usually produces meaningful results. In this paper, given two points and cardinality of fuzzy partition, we prove that it is not true that there always exists a fuzzy partition with fuzzy equalization in which two of points having peaks fall on the given two points. Then, we establish an algorithm that minimizes the maximum distance between given two points and adjacent points having peaks in the partition. A numerical example is presented to show the validity of the suggested algorithm.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.217-220
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• 2001

The fuzzy equalization method does not require the usual learning step for generating fuzzy rules. However it is heavily depend on the given input-output data set. So, we adapt an hierarchical scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership functions obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to the Rice taste data and got better results than previous ones.

• 한국지능시스템학회논문지
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• 제11권9호
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• pp.827-832
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• 2001

본 논문은 수치적인 데이터를 이용하여 시스템을 구성하는 퍼지 모델링에서 각각의 장점들을 유지하면서 순차적으로 성능을 개선하는 방법을 제안한다. 기존의 다양한 퍼지 모델링의 최적화 방법들은 각각의 뛰어난 최적화 기법을 이용하면서도 순차적으로 퍼지 모델의 성능을 개선하려하는 시도는 많지 않았다. 이에 본 논문에서는 각 단계별로 최적의 성능을 구현하고 이를 다음 단계에서 초기로 이용함으로써 퍼지 모델의 성능이 순차적으로 개선되는 것을 제안하였다. 이는 각각의 최적화 기법들을 지속적으로 이용함으로써 원하는 모델의 성능을 개선하고자 하는 것이다. 제안된 방법의 유용성을 Rice taste 데이터 모델에 적용하여 제안된 방법이 이전의 연구보다 좋은 결과를 보임을 알았다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.806-817
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• 2015

The contrast limited adaptive histogram equalization(CLAHE) is an advanced method for the histogram equalization which is a common contrast enhancement technique. The CLAHE divides the image into sections, and applies the contrast limited histogram equalization for each section. X-ray images can be classified into three areas: skin, bone, and air area. In clinical application, the interest area is limited to the skin or bone area depending on the diagnosis region. The CLAHE could deteriorate X-ray image quality because the CLAHE enhances the area which doesn't need to be enhanced. In this paper, we propose a new method which automatically determines the clip limit of CLAHE's parameter to improve X-ray image quality using fuzzy logic. We introduce fuzzy logic which is possible to determine clip limit proportional to the interest of users. Experimental results show that the proposed method improve images according to the user's preference by focusing on the subject.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.121-125
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• 2001

본 논문에서는 자동적인 퍼지 규칙 생성을 위해 퍼지 균등화(Fuzzy Equalization)와 유전알고리즘(Genetic Algorithm)을 이용한 TSK 퍼지 시스템의 구축을 다룬다. Pedrycz에 의해 제안된 퍼지 균등화 방법은 수치적인 데이터로부터 확률분포함수를 구축한 후 전체공간상에서 이들을 적절히 표현할 수 있는 소속함수를 생성한다. 이렇게 구축된 각 입력에 대한 소속함수는 유전알고리즘에 의해 입력공간이 분할되며 결론부 파라미터는 최소자승법에 의해 추정되어 진다. 제안된 방법은 그리드 분할로 인해 규칙의 수가 증가하는 문제를 해결하고 학습데이터와 검증데이터에 의해 타당한 입력공간분할과 퍼지 규칙을 생성할 수 있다. 시뮬레이션의 예로서 Box-Jenkins의 가스로 데이터의 모델링에 적용하여 제안된 방법의 유용성을 알 수 있다.

• Journal of information and communication convergence engineering
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• 제7권3호
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• pp.383-391
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• 2009

In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.

• 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.

• 한국멀티미디어학회논문지
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• 제14권8호
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• pp.965-980
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• 2011

In this paper, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is investigated for blind channel equalization. In the proposed CFCM, a collection of clustered centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next fuzzy clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the "conventional" Fuzzy C-Means (FCM).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.957-959
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• 1999

In this paper we proposed a new fuzzy modeling method by Fuzzy Equalization(FE) based on probability theory. FE concerns a process of building membership function without learning using back-propagation of neural network. Therefore, we compare the proposed method with Adaptive Network-based Inference System based on hybrid learning. Finally, we will show better performance and its usefulness for a new fuzzy modeling to automobile mpg prediction.