• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.508-514
• /
• 2021

In this paper, to propose a classifier based on supervised learning, three types of fuzzy membership functions that determine the membership of each feature of classification data are proposed. In addition, the possibility of improving the classifier performance was suggested by using the average value calculation method used in the process of deriving the classification result using the average value of the membership degrees for each feature, not by using a simple arithmetic average, but by using a weighted average using various weights. To experiment with the proposed methods, three standard data sets were used: Iris, Ecoli, and Yeast. As a result of the experiment, it was confirmed that evenly excellent classification performance can be obtained for data sets of different characteristics. It was confirmed that better classification performance is possible through improvement of fuzzy membership functions and the weighted average methods.

• The Journal of the Korea Contents Association
• /
• v.11 no.4
• /
• pp.74-82
• /
• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.105-111
• /
• 2009

Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself - the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1998.11a
• /
• pp.202-207
• /
• 1998

본 논문에서는 유전자 알고리즘을 이용하여 불확실한 비선형 시스템의 퍼지-신경 회로망 모델링을 제안하였다. 제안한 퍼지-신경 회로망 모델링을 위한 학습 알고리즘은 다음과 같은 세 단계로 나누어 진행한다. 첫 번째 단계에서는 퍼지 모델의 소속 함수의 중심간과 표준편차를 구하여 초기 퍼지소속 함수를 결정한다. 두 번째 단계에서는 새로운 알고리즘을 통하여 언어적 퍼지 규칙을 만든다. 마지막 세 번째 단계에서는 유전자 알고리즘을 이용하여 중심값과 표준편차를 최적화함으로써 퍼지 모델의 소속 함수를 조절한다. 제안된 유전자 알고리즘의 장점은 흔히 신경 회로망에서 널리 쓰이는 역전파 알고리즘이 갖는 지역 최소점에 빠지는 현상이 없다는 것이다. 제안한 알고리즘의 유용성을 확인하기 위하여 일반적으로 가장 많이 쓰이는 비선형 시스템에 대하여 시뮬레이션 하여 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.1
• /
• pp.201-208
• /
• 2017

This paper propose triangular fuzzy membership function to make model that based on awareness of human in the love model that with external force, which have the basic love model of Romeo and Juliet. This paper represents the phenomena of behaviors by time series and phase portraits after using this fuzzy triangular membership function as an external force and also confirms existence of nonlinear characteristics.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2002.05a
• /
• pp.86-89
• /
• 2002

본 연구는 객관적인 생리 신호로부터 인간의 감성을 추론할 수 있는 감성 평가 전문가 시스템을 개발하기 위한 첫 번째 단계로, 측정된 생리 신호를 이용하여 인간의 긴장도를 판단하는 알고리즘의 개발이 목표이다. 감성 평가에 관련된 애매함을 수리적으로 취급하기 위해 퍼지 이론을 적용하여 임의의 감성 영역에 속하는 정도를 소속 함수로 정량화 함으로써 감성 평가를 가능하게 하고자 하였다 소속 함수의 결정은 상상을 통해 유발된 긴장/이완의 생리 신호 데이터 베이스 결과를 사용하였다. 그리고 두 가지 이상의 생리 신호 측정 결과와 각 생리 신호의 소속 함수로부터 하나의 최종 결과 (긴장도)를 유추하기 위해서 Dempster-Shafer 증거합 법칙을 적용하였고, 이를 통해 최종적인 긴장도를 도출할 수 있도록 하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.3 no.2
• /
• pp.383-388
• /
• 1999

In this paper, we propose the fuzzy-neural controller with genetic algorithm(GA) for precise on-line control. We design the proposed controller having a ability to adjust membership function for a plant by advanced algorithm of fuzzy-neural network after approximative one being completed by genetic algorithm. Finally we compare the result for a speed control of DC servo motor by the proposed controller with GA-fuzzy one in order to evaluate its performance and precision.

• Journal of Intelligence and Information Systems
• /
• v.6 no.1
• /
• pp.65-72
• /
• 2000

In this paper we propose the new binary image matching algorithm called the Fuzzy logic induced Hausdorff Distance(FHD) for finding the maximally matched image with the query image. The membership histogram is obtained by normalizing the cardinality of the subset with the corresponding radius after obtaining the distribution of the minimum distance computed by the Hausdroff distance between two binary images. in the proposed algorithm, The fuzzy influence method Center of Gravity(COG) is applied to calculate the best matching candidate in the membership function described above. The proposed algorithm shows the excellent results for the face image recognition when the noise is added to the query image as well as for the character recognition.

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.2
• /
• pp.209-214
• /
• 1995

It is very hard to identify the fuzzy rules and tune the membership functions of the fuzzy reasoning in fuzzy systems modeling .We propose a method which canautomatically identify the fuzzy rules and tune the membership functions of fuzzy reasoning simultaneously using artifical neural network. In this model,fuzzy rules are identified by backpropagation algorithm. The feasibility of the method is simulated by a simple robot manipulator.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.523-528
• /
• 2002

기존의 자기 주도적 학습 평가들은 대부분의 선다형 또는 단답형 문항에 대해서 학습평가가 시험 점수로 제공되고, 학습 평가의 정도를 객관적으로 평가 할 수 얼어 학습의 효율성에 대해서 부정적인 시각도 있다. 본 논문에서는 학습자 스스로가 학습 능력 평가를 객관적으로 평가하기 위해 퍼지 이론의 삼각형 타입 소속 함수를 이용한 자기 주도적 학습 평가 방법을 제안한다. 제안된 자기 주도적 학습 평가 방법은 학습에 대해 시험 결과를 세 개의 퍼지 등급으로 분류하여 소속도를 계산하고 퍼지 등급표를 적용하여 최종 퍼지 등급도에 따라 시험 결과를 평가하는 방법을 제시한다.