• 한국컴퓨터정보학회논문지
• /
• 제3권3호
• /
• pp.55-61
• /
• 1998

CAI(Computer Aided Instruction)에서 학습의 데이터가 되는 교재의 학습 순서는쉬운 항목에서 어려운 항목의 순번으로 나열되어 있다. 학습자는 반드시 이 순서대로 학습하는 것은 아니다. 실제는 항목간의 전후를 시행착오 하면서 학습을 하고 있다. 본 논문에서는 지적 CAI(Intelligent CAI : ICAI) 학습으로 항목에 대한 이해도를 퍼지성의 시행착오로학습시켜 구조화된 학습을 퍼지 추론에 의해 모델화 한다. 방법으로는 각 항목간의 순서관계에 의해 학습과 이해의 차이를 고려하여 퍼지 추론 규칙에 의해 학습의 모델을 정식화했다. 추론 규칙을 간략화 하여 CAL 시스템의 처리로 시행착오의 학습을 제안한다.

• KIEE International Transactions on Power Engineering
• /
• 제3A권4호
• /
• pp.191-197
• /
• 2003

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, such as inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if undetected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, their modeling is difficult and numerous papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients program (EMTP). This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square (RMS) values of 3-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results demonstrate that the ANFIS can detect and classify faults including LIFs and HIFs accurately within half a cycle.

• 대한인간공학회지
• /
• 제14권1호
• /
• pp.97-104
• /
• 1995

The widespread use of VDU has improved the efficiency of information transmission between man and machine, but has caused new occupational health and ergonomics problems. In this study, we tried to construct a fuzzy hyman performance model of VDU workers in Korea. Fuzzy inferences of human perfor- mance are obtained from the fuzzy inference rule with the job difficulty, CFF, SACL, Type A. and the degree of concentration in VDU work. Eight healthy female undergraduate students at Kyungnam university for subjects aged 20 to 23 years were examined in this experiment. They calculated continuous addition, subtraction, and multiplication of 1 or 2 digit numbers that were produced randomly on the CRT. Subjects peoformed two types of a numeric operation, which easy and difficult work produced 400 and 600 problems within a 40 minute work session, respectively. Subjects were tested over two workdays according to the type of work(easy and difficult) consisting of four 40 minutes work sessions in the morning. Each work lasted for five minutes with a ten minutes rest break. 117 fuzzy inference rules were obtained from the experimental data. The value of consequent part was obtained by a descent method. The difference between real human error and estimated value of fuzzy inference was $1.8075{\pm}1.8591%(M{\pm}SD)$. The difference in easy and diffcult works were $2.69{\pm}2.13%$ and $0.92{\pm}0.93%$, respectively.

• 한국통신학회논문지
• /
• 제16권1호
• /
• pp.23-34
• /
• 1991

본 논문에서는 fuzzy 추론에 의한 중복물체인식에 관해 연구하였다. 영상은 다각형 근사방법을 이용하여 선형선소들의 집합으로 변환되었으며 각 선형선소는 물체의 경계점으로부터 추출된 국부특징점으로 구성되었다. 또한 추출된 특징량을 정보의 불확실성을 나타내는 fuzzy 개념과 대응시킨 fuzzy화 데이터로 나타내었으며, 미지영상에 있어서 모델의 인식은 모델영상으로 부터 생성된 생성규칙을 이용하여 fuzzy 추론에 의해 이루어졌다. 실험을 통하여 불확실성의 정도 변화에 따른 인식 결과의 변화를 고찰하였으며, 120개의 모델이 포함되어 있는 30개의 미지영상에 대해 92.5%의 인식률을 얻었다.

• 한국산업융합학회 논문집
• /
• 제22권4호
• /
• pp.427-433
• /
• 2019

This paper propose a fuzzy inference model for map building and navigation for a mobile robot with an active camera, which is intelligently navigating to the goal location in unknown environments using sensor fusion, based on situational command using an active camera sensor. Active cameras provide a mobile robot with the capability to estimate and track feature images over a hallway field of view. In this paper, instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. Command fusion method is used to govern the robot navigation. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of active camera sensor for navigation experiments are fused into the identification process. Navigation performance improves on that achieved using fuzzy inference alone and shows significant advantages over command fusion techniques. Experimental evidences are provided, demonstrating that the proposed method can be reliably used over a wide range of relative positions between the active camera and the feature images.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
• /
• pp.47-50
• /
• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• 대한조선학회논문집
• /
• 제57권5호
• /
• pp.305-311
• /
• 2020

This study presents a probabilistic time series forecast of ship structural response using Bayesian inference combined with Volterra linear model. The structural response of a ship exposed to irregular wave excitation was represented by a linear Volterra model and unknown uncertainties were taken care by probability distribution of time series. To achieve the goal, Volterra series of first order was expanded to a linear combination of Laguerre functions and the probability distribution of Laguerre coefficients is estimated using the prepared data by treating Laguerre coefficients as random variables. In order to check the validity of the proposed methodology, it was applied to a linear oscillator model containing damping uncertainties, and also applied to model test data obtained by segmented hull model of 400,000 DWT VLOC as a practical problem.

• 인지과학
• /
• 제13권1호
• /
• pp.13-21
• /
• 2002

퍼지 논리를 이용한 추론은 일부의 정보가 무시되어 적절하지 못한 추론 결과를 초래할 수 있다. 또한 신경망은 패턴 처리에는 적합하지만 인간의 지식을 모델링하기 위해서 필요한 논리적인 추론에는 부적합하다. 하지만 신경 망의 변형인 신경 논리 망을 이용하면 논리적인 추론이 가능하다. 따라서 본 논문에서는 기존의 신경 논리 망을 기반으로 하는 추론 망을 확장하여 퍼지 추론 망을 구성하고 기존의 추론 망에서 사용되는 전파규칙을 보완하여 적용하고자 한다. 퍼지 추론 망에서 퍼지 규칙의 결론부에 해당하는 명제의 믿음 값을 결정하기 위해서는 추론하고자 하는 명제에 연결된 노드들을 탐색해야 한다. 이를 위해, 연결된 모든 노드들의 링크를 따라 순차적인 탐색을 하는 경우와 링크에 부여된 우선순위에 의해 탐색을 하는 경우의 탐색비용에 대하여 실험을 통해 비교 평가하였다. 실험결과 퍼지 추론 망의 크기가 확장될수록, 그리고 탐색 경험의 횟수가 증가할수록 순차적인 탐색전략보다 우선순위에 의한 탐색전략이 탐색 비용면에서 효율성이 더욱 증가함을 알 수 있었다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.120-123
• /
• 1997

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제10권4호
• /
• pp.314-318
• /
• 2010

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (MLP) models as the likelihood for secondary structure prediction of proteins. With varying window sizes of neighboring amino acid information, the information is extracted and passed back and forth between the Neural Net and the Bayesian Inference process until the posterior probability of the secondary structure converges.