• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.1
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• pp.3-12
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• 1994

본 연구는, 복잡한 비선형시스템의 표현에 뛰어난 능력을 갖고 있는 TSK형 퍼지모델로 부터 전체시스템의 안정성을 보장할 수 있는 퍼지상태제어기의 설계방법을 제안한다. 그 퍼지상태제어기는 TSK형 퍼지모델과 같은 형태의 퍼지규칙들로 구성되며, 추론 방법 및 상태제어 파라미터 행렬을 구하는 방벙은 전체시스템의 상태천이행렬이 원하는 안정한 것이 될 수 있도록 정해진다. 또한 본 논문에서는, 현재 대부분이 숙련가에 의해 수동으로 조작되고 있는 컨테이너 크래인의 새로은 제어 방법을 제시하고 제안한 퍼지상태제어기를 적용한다.컨테이너 크래인의 모형을 만들어, 제어숙련가의 수동조작으로 결정되는 트롤리와 승강기의 규범속도를 퍼지모델로 표현하고, 트롤리와 승강기가 그 규범 속도에 따르도록 제안한 퍼지상태제어기로 제어한다. 제안된 방법을 실험한 결과 모형크레인의 궤적이 숙련가에 의해 만들어진 궤적과 매우 유사하게 됨을 알 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.408-414
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• 2009

A GPS/INS system is widely used in the UGV to estimate position during the mission. However, there are few restrictions when a GPS/INS system used alone. For example, GPS provides precise location information but easily interrupted by external factors like weather, environment, etc. INS provides continuous location data but positioning errors grew very rapidly with time. Therefore, it is necessary to integrating multi-sensors for more continuous and correct position estimation. In this paper, we propose location estimation algorithm of the UGV for GPS/INS integrated system that combines Fuzzy Inference and Unscented Particle Filter(UPF) to improve navigation. Fuzzy inference provides the simplest method integrating GPS/INS and UPF is non-linear estimation filter well suited to the correction of errors. The performance of the proposed algorithm was tested to compare with other algorithms. the results show that the proposed algorithm is more accuracy in position estimation and ensures continuous position tracking.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.3
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• pp.132-136
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• 2014

We introduce a new architecture of TSK-based fuzzy inference system. The proposed model used fuzzy c-means clustering method(FCM) for efficient disposal of data. The premise part of fuzzy rules don't assume any membership function such as triangular, gaussian, ellipsoidal because we construct the premise part of fuzzy rules using FCM. As a result, we can reduce to architecture of model. In this paper, we are able to use four types of polynomials as consequence part of fuzzy rules such as simplified, linear, quadratic, modified quadratic. Weighed Least Square Estimator are used to estimates the coefficients of polynomial. The proposed model is evaluated with the use of Boston housing data called Machine Learning dataset.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.406-409
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• 2002

본 논문에서는 문자인식이 가능하도록 줌, 포커스를 제어하여 한글 문서 영상을 확대/축소하는 시스템을 구현하였다. 한글 문서 영상에서 확대/축소 할 영역이 지정되면 그 영역의 가로, 세로 거리를 펄스 수로 변환한 후 Step모터를 제어하여 그 위치만큼 카메라를 이동시킨다. 문서 영상이 입력되면 문자인식이 가능한 크기만큼 줌을 제어하고, 피드백 되어진 영상으로부터 조정된 줌에 맞는 포커스로 근접 제어한 후, 더욱 선명한 영상을 얻기 위해 명암 차이에 의한 미세 조정을 하였다. 이 경우, 줌 및 포커스는 퍼지 추론으로 .제어하는 DC모터로 조정하였다.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• Journal of Internet Computing and Services
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• v.4 no.2
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• pp.61-67
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• 2003

It has recently become known that the social issue of obesity, caused by increased caloric intake and lack of exercise, is a risk factor in the cause of various adult diseases. Above all, to prevent or cure obesity, we must accurately evaluate the degree of obesity, and we have used BML, WHR, and waist measurements for this purpose. In this paper, we propose an obesity evaluation system based on fuzzy inference using BML and waist measurement. For this purpose, we decided reasoning rule and membership function about BML and waist measurements. The inference result is presented in a descriptive sentence.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.1-9
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• 2008

Fuzzy Cognitive Map, one of ways to model, describe and infer reasoning relations, is widely used in the field of reasoning knowledge engineering. Despite of the natural and easy understanding of decision and smooth explanation of relation between front and rear, reasoning relation is organized with mathematical haziness and complex algorithm and rarely has an interactive user interface. This paper suggests an interactive Fuzzy Cognitive Map(FCM) construction support system. It builds a FCM increasingly concerning multiple experts' knowledge. Futhermore, it supports user-supportive environment by dynamically displaying the structure of Fuzzy Cognitive Map which is constructed by the interaction between experts and the system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.35-36
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• 2012

The generation of fuzzy rules is inevitable in order to construct fuzzy modeling and in general, has the problem that the number of rules increases exponentially with increasing dimension. To solve this problem, we introduce the system that generate the fuzzy rules and make a inference based on FCM clustering algorithm that partition the input space in the scatter form. The parameters in the premise part of the fuzzy rules is determined as membership matrix by the FCM clustering algorithm and the consequence part of the fuzzy rules is are expressed as a polynomial function. Proposed model evaluated using the numerical data.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.139-148
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• 1995

In this paper an electro-hydraulic servo system is designed by using a fuzzy control algorithm. In order to drive an optimal fuzzy control system, a simulation program for the control system has been developed. By this program the fuzzifier and defuzzifier, a fuzzy inference method, a fuzzy relational matrix, and a fuzzy inference method are investigated. As a result, Larsen inference method, 9*9 fuzzy relational matrix, and center of area defuzzifier are turned out the best as parameters. Finally this method is compared with the conventional PID algotithm, and showed that the fuzzy control performs better than PID algorithm. The fuzzy control performs very well adap- tation against uncertain disturbances.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.970-976
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• 2007

In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.