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

• 한국정보처리학회논문지
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• 제2권2호
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• pp.209-214
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• 1995

일반적으로 퍼지 시스템 모델링에 있어서, 퍼지 규칙을 인식하고 퍼지 추론의 소속함수를 조정하기란 매우 어렵다.본 논문에서는 인공신경망을 이용함으로써,자동으로 퍼지 규칙을 인식하고 동시에 퍼지 추론의 소속함수를 조정할수 있는 방법을 제시하였다. 본 모델은 역전파를 기본으로 한 알고리즘으로 학습하며,이 방법의 타당성을 로보트 매니퓰레이터를 통해 검증한다.

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

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

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(5)
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• pp.273-276
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• 2002

This paper has design of ECG pattern classification system using decision of fuzzy IF-THEN rules and neural network. each fuzzy IF-THEN rule in our classification system has antecedent lingustic values and a single consequent class. we use a fuzzy reasoning method based on a single winner rule in the classification phase. this paper in, the MIT/BIH arrhythmia database for the source of input signal is used in order to evaluate the performance of the proposed system. From the simulation results, we can effectively pattern classification by application of learned from neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1804-1807
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.70-70
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• 2000

Recently, a neuro-fuzzy approach, a combination of neural networks and fuzzy reasoning, has been playing an important role in the motor control. In this paper, a novel method of fiction compensation using neuro-fuzzy architecture has been shown to significantly improve the performance of a DC motor system with nonlinear friction characteristics. The structure of the controller is the neuro-fuzzy network with the TS(Takagi-Sugeno) model. A back-propagation neural network based on a gradient descent algorithm is employed, and all of its parameters can be on-line trained. The performance of the proposed controller is compared with both a conventional neuro-controller and a PI controller.

• 한국통신학회논문지
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• 제27권7C호
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• pp.664-669
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• 2002

본 논문에서는 ATM 망에서의 접속된 트래픽에 대해 효율적인 버퍼제어를 위한 퍼지제어 알고리즘을 제안한다. 제안된 퍼지제어 알고리즘은 동적 임계값을 구하기 위해 두 개의 우선순위와 퍼지집합을 사용한다. 즉, 발생된 저, 고순위 트래픽 비율에 따라 퍼지집합 이론을 통하여 추론한 후 그 비퍼지화값으로 접속된 트래픽에 대해 버퍼에서의 임계값을 제어하도록 하였다. 성능분석 결과 기존의 부분버퍼공유기법에서보다 셀손실율 면에서 그 성능이 향상됨을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권4호
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• 한국통신학회논문지
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• 제18권2호
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• pp.231-238
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• 1993

퍼지제어룰은 일반적으로 시스템에 대한 전문오퍼레이터나 기술자가 갖고 있는 애매모호함을 포함하고 있는 제어지식을 시스템의 입 출력 분할에 의해 if-then이라는 언어적 룰로서 표현하는 것으로 전문오퍼레이터나 기술자의 제어지식 자체의 부정확과 룰의 불완전등으로 완전하게 표현한다는 것은 대단히 어렵다. 이러한 불완전한 룰의 정확도를 시스템 동작 후에도 연속적으로 높이기 위한 방법으로서 신경회로망에 의한 퍼지 추론과 학습을 제시한다. 이 방식은 시스템의 퍼지롤의 후건부를 층상신경회로망의 역전파(Back-propagation) 학습방법에 의한 정확도를 증진시키고, 전건부의 적합도를 연상기억방식에 의해 추론하는 방식으로서, 이 방식을 이용하여 한정된 구역 내에서 숙련된 기술과 지식이 필요한 차의 안전하고 신속한 정차를 위한 Auto-Parking Fuzzy Controller를 설계하고 시뮬레이션을 통해 그 타당성을 입증하였다.