• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.107-111
• /
• 1998

This paper presents a now approach to the design of intelligent contorl system 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. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. 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 dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.69.3-69
• /
• 2001

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 learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection 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.

• Nuclear Engineering and Technology
• /
• 제53권11호
• /
• pp.3675-3684
• /
• 2021

Human reliability analysis (HRA) is a proactive approach to model and evaluate human systematic errors, and has been extensively applied in various complicated systems. Dependence assessment among human errors plays a key role in the HRA, which relies heavily on the knowledge and experience of experts in real-world cases. Moreover, there are ofthen different types of uncertainty when experts use linguistic labels to evaluate the dependencies between human failure events. In this context, this paper aims to develop a new method based on linguistic hesitant fuzzy sets and the technique for human error rate prediction (THERP) technique to manage the dependence in HRA. This method handles the linguistic assessments given by experts according to the linguistic hesitant fuzzy sets, determines the weights of influential factors by an extended best-worst method, and confirms the degree of dependence between successive actions based on the THERP method. Finally, the effectiveness and practicality of the presented linguistic hesitant fuzzy THERP method are demonstrated through an empirical healthcare dependence analysis.

• Smart Structures and Systems
• /
• 제20권1호
• /
• pp.61-74
• /
• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• 한국정보통신학회논문지
• /
• 제9권4호
• /
• pp.883-889
• /
• 2005

다층 구조 신경망에서 널리 사용되는 오류 역전파 알고리즘은 초기 가중치와 불충분한 은닉층의 노드 수로 인하여 지역 최소화에 빠질 가능성이 있다. 따라서 본 논문에서는 오류 역전파 알고리즘에서 은닉층의 노드 수를 설정하는 문제와 ART-1에서 경계 변수의 설정에 따라 인식률이 저하되는 문제점을 개선하기 위하여 ART-1과 퍼지 단층 지도 학습 알고리즘을 결합한 ATR-1 기반 퍼지 다층 지도 학습 알고리즘을 제안 한다. 자가 생성을 이용한 제안된 퍼지 지도 학습 알고리즘은 입력층에서 은닉층으로 노드를 생성시키는 방식은 ART-1을 적용하였고, 가중치 조정은 특정 패턴에 대한 저장 패턴을 수정하도록 하는 winner-take-all 방식을 적용하였다. 제안된 학습 방법의 성능을 평가하기 위하여 주민등록증 영상을 대상으로 실험한 결과, 기존의 오류 역전파 알고즘보다 연결 가중치들이 지역 최소화에 위치할 가능성이 줄었고 학습 속도 및 정체 현상도 개선되었다.

• 한국건설관리학회논문집
• /
• 제5권2호
• /
• pp.115-122
• /
• 2004

이 논문은 건설공사 과정에서 불확실성과 위험성이 비교적 높은 토공사, 지정공사 및 기초공사에서 발생할 수 있는 리스크인자의 중요도 산정에 관한 것이다. 이 연구는 리스크 $식별\cdot분석\cdot대응$으로 이루어지는 리스크관리 3단계 중 리스크 식별과 분석단계를 중심으로 연구를 진행하였다. 리스크 식별은 기존의 건설공사 작업분류체계를 참고하여 대상 공종을 $공통\cdot토공사\cdot지정$ 및 기초공사로 구분하여 초기 건설공사의 리스크 분류체계를 제시하였다. 리스크 분석은 리스크분류체계를 바탕으로 퍼지이론에 기반하여 실시하였다. 리스크인자의 중요도는 AHP기법에 의한 상대적 중요도와 퍼지척도로부터 구한 리스크인자들 사이의 절대적 중요도를 고려하여 산정하였으며 리스크 인자의 최종적인 중요도는 Sugeno $\lambda$-퍼지척도를 사용하여 구하였다.

• 에너지공학
• /
• 제16권3호
• /
• pp.128-148
• /
• 2007

원자력 연구개발사업의 품질보증은 사업 고유의 속성을 반영하지 못하는 무분별한 품질보증요건의 선정 및 적용으로 인해 불필요한 요건이 포함되거나 필요한 요건이 배제될 가능성이 있을 뿐만 아니라, 이로 인한 자원낭비의 비경제성이 우려되고 있다. 본 연구에서는 원자력 연구개발사업의 속성을 반영하는 품질보증요건의 중요도 평가방법론을 제시하였다. 이 방법론은 기본적인 분석틀로서 QFD(Quality Function Deployment)를 활용하며, 계산과정에서 퍼지개념을 도입하여 인간 판단의 모호함에 따른 불확실성을 결과에 반영하고, 피설문자의 응답확신도를 활용함으로써 설문의 내용을 보다 유용하게 분석에 반영할 수 있다는 특징을 가지고 있다. 특정 원자력 연구개발사업에 제시된 방법론을 적용함으로써 그 실용성을, 다양한 시나리오 분석을 통해 유효성을 확인하였다. 시나리오 분석 결과에 따르면, 퍼지개념과 응답확신도를 활용함에 따라 기존의 방법론과 비교하여 품질보증요건의 중요도에 유의할만한 수준의 변화를 가져올 수 있음을 확인하였다. 또한, 사업 속성요인의 수준변화가 품질보증요건의 중요도에 직간접적으로 반영되고 있음을 확인하였다. 본 연구는 원자력 연구개발사업의 속성을 반영하는 품질보증요건 중요도 산출방법론이라는 점과 QFD의 새로운 응용분야를 개척하였다는 점에서 그 의미를 찾을 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권2호
• /
• pp.169-174
• /
• 2005

A neural networks (NN) saturation compensation scheme for DC motor systems is presented. The scheme that leads to stability, command following and disturbance rejection is rigorously proved. On-line weights tuning law, the overall closed loop performance and the boundness of the NN weights are derived and guaranteed based on Lyapunov approach. The simulation and experimental results show that the proposed scheme effectively compensate for saturation nonlinearity in the presence of system uncertainty.

• International Journal of Control, Automation, and Systems
• /
• 제3권4호
• /
• pp.552-563
• /
• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2254-2259
• /
• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.