• 한국통신학회논문지
• /
• 제29권8C호
• /
• pp.1149-1158
• /
• 2004

본 논문에서는 ATM 망에서의 효율적인 트래픽 제어를 위하여 언어적인 규칙과 퍼지 추론부로 구성되는 퍼지로직에서 퍼지 규칙을 생성하였다. 퍼지 규칙 내부에 포함된 제어 파라메터들은 주어진 성능 함수를 최소화하도록 학습된다 즉, 발생된 저, 고순위 트래픽 도착 비율에 따라 퍼지집합 이론을 통하여 추론한 후 그 비퍼지화값으로 접속된 트래픽에 대해 버퍼에서의 임계값을 제어하도록 하였다. 또한, 생성된 퍼지 규칙의 타당성을 검증하기 위하여 MATLAB6.5에서와 온라인 빌드업으로 규칙에 대한 실험결과를 보인다. 그 결과, 고, 저 트래픽 도착 비율에 따라 효율적으로 버퍼에서의 임계값이 제어됨을 확인하였다.

• 정보처리학회지
• /
• 제11권6호
• /
• pp.56-75
• /
• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• International Journal of Control, Automation, and Systems
• /
• 제6권5호
• /
• pp.755-766
• /
• 2008

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
• /
• pp.417-420
• /
• 1990

The conventional quantitative techniques of system analysis are intrinsically unsuited for dealing with humanistic systems. Therefore, the rule based modeling of fuzzy linguistic type has been developed for the analysis of humanistic systems and complex systems and it is very significant for analysis and design of fuzzy logic controller. The activated sludge process is a commonly used method for treating sewage and waste waters. A mathematical tool to build a fuzzy model of the activated sludge process where fuzzy implications and linear reasoning are used is presented in here. A root-mean square error is used as the criterion of the fuzzy model's adequacy to the A.S.P. and the least square method is used for the identification of optimum consequence parameters. A method of modeling of the activated sludge process using its input-output data and simulation results for its application are shown.

• 한국통신학회논문지
• /
• 제28권10B호
• /
• pp.854-861
• /
• 2003

본 논문에서는 ATM 망에서의 접속된 트래픽에 대해 효율적인 버퍼제어를 위한 퍼지제어 알고리즘을 제안한다. 제안된 퍼지 제어 알고리즘은 동적 서비스율을 구하기 위해 트래픽의 도착율과 버퍼점유률 그리고 퍼지집합을 사용한다. 즉, 발생된 전체 트래픽의 도착율과 버퍼점유률에 따라 퍼지논리를 기반으로 하여 추론한다. 그 후, 추론 결과로 얻어진 비퍼지화값으로 접속된 트래픽에 대해 서버에서의 서비스율을 제어하도록 하였다. 성능분석 결과 기존의 부분버퍼공유기법과 비교하여 셀손실율을 줄임으로서 그 성능이 향상되었다.

• Journal of Information Processing Systems
• /
• 제6권2호
• /
• pp.209-218
• /
• 2010

Classical warranty plans require crisp data obtained from strictly controlled reliability tests. However, in a real situation these requirements might not be fulfilled. In an extreme case, the warranty claims data come from users whose reports are expressed in a vague way. Furthermore, there are special situations where several characteristics are used together as criteria for judging the warranty eligibility of a failed product. This paper suggests a fast reasoning model based on fuzzy logic to handle multi-attribute and vague warranty data.

• 한국정보통신학회논문지
• /
• 제8권5호
• /
• pp.1050-1057
• /
• 2004

연구의 목적은 컴퓨터 도움 없이 독립으로 서보시스템의 퍼지제어를 위한 퍼지제어기 하드웨어 회로 개발이다 본 논문은 $\alpha$-레벨 퍼지집합 분해에 기초하여 DC 서보 시스템의 퍼지제어를 위해 퍼지 추론 연산의 하드웨어에 대하여 나타내었다. 퍼지추론에서 비퍼지화까지 일체적으로 퍼지추론 연산에 의해 직접 PWM 조작신호를 얻는 방법이 제안되었다. 이 방법은 아날로그 회로로 쉽게 구현할 수있다. 퍼지제어기 입출력 특성과 직류서보 전동기 퍼지제어 응답특성에서 $\alpha$-레벨 양자화 효과에 대하여 검토한 결과 양자화 수 $\alpha$=4 단계가 충분한 것을 알 수 있다. 제안한 하드웨어 방법은 실 직류 서보시스템의 적용에서 실험을 통하여 그 효과를 나타내었다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.15-15
• /
• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1997년도 추계학술대회 논문집
• /
• pp.70-75
• /
• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제1권1호
• /
• pp.13-23
• /
• 2001

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Hence, these systems are inflexible in the sense that they cannot easily be adapted to other applications. In this paper, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify \\\"defects\\\" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning if knowledge that allows concurrent parallel processing during recognition.cognition.