• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.539-541
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• 2006

Biped locomotion is a popular research area in robotics due to the high adaptability of a walking robot in an unstructured environment. When attempting to automate the motion planning process for a biped walking robot, one of the main issues is assurance of dynamic stability of motion. This can be categorized into three general groups: body stability, body path stability, and gait stability. A zero moment point (ZMP), a point where the total forces and moments acting on the robot are zero, is usually employed as a basic component for dynamically stable motion. In this rarer, learning based neuro-fuzzy systems have been developed and applied to model ZMP trajectory of a biped walking robot. As a result, we can provide more improved insight into physical walking mechanisms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.772-777
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• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• Journal of Astronomy and Space Sciences
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• 제29권4호
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.168-171
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• 2006

본 논문에서는 Particle Swarm Optimization 기법을 이용한 뉴로-퍼지 시스템의 파라미터 동정을 실시한다. PSO의 학습 및 군집 특성을 이용하여 시스템을 학습한다. 유전 알고리즘과 같은 무작위 탐색법을 이용하며 하나의 해 군집에 대해 다수 객체들이 탐색하는 기법을 통하여 최적해 부분의 탐색성능을 높여 전체 모델의 학습성능을 개선하고자 한다. 제안된 기법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.298-301
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• 1996

This paper describes the structure Identification of nonlinear function using Adaptive Neuro-Fuzzy Inference Technique(ANFIS). Nonlinear mapping relationship between inputs and outputs were modeled by Sugeno-Takaki's Fuzzy Inference Method. Specially, the consequent parts were identified using a series of 1st order equations and the antecedent parts using triangular type membership function or bell type ones. According to learning Rules of ANFIS, adjustable parameters were converged rapidly and accurately.

• 한국컴퓨터정보학회논문지
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• 제9권1호
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• pp.37-43
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• 2004

컴퓨터 네트워크의 확대 및 인터넷 이용의 급속한 증가에 따라 컴퓨터 보안문제가 중요하게 되었다 따라서 침입자들로부터 위험을 줄이기 위해 침입탐지 시스템에 관한 연구가 진행되고 있다. 본 논문에서는 네트워크 기반의 이상 침입 탐지를 위하여 뉴로-퍼지 기법을 적용하고자 한다 불확실성을 처리하는 퍼지 이론을 이상 침입 탐지영역에 도입하여 적용함으로써 오용 탐지의 한계성을 극복하여 알려지지 않은 침입탐지를 하고자 한다.

• Structural Engineering and Mechanics
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• 제51권2호
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• pp.249-265
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• 2014

The mechanical characteristics of materials are very essential in structural analysis for the accuracy of structural calculations. The estimation modulus of elasticity of concrete ($E_c$), one of the most important mechanical characteristics, is a very complex area in terms of analytical models. Many attempts have been made to model the modulus of elasticity through the use of experimental data. In this study, the neuro-fuzzy (NF) technique was investigated in estimating modulus of elasticity of concrete and a new simple NF model by implementing a different NF system approach was proposed. A large experimental database was used during the development stage. Then, NF model results were compared with various experimental data and results from several models available in related research literature. Several statistic measuring parameters were used to evaluate the performance of the NF model comparing to other models. Consequently, it has been observed that NF technique can be successfully used in estimating modulus of elasticity of concrete. It was also discovered that NF model results correlated strongly with experimental data and indicated more reliable outcomes in comparison to the other models.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.257-262
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• 2009

Image processing has been utilized for assessment of infrastructure surface coating conditions for years. However, there is no robust method to overcome the non-uniform illumination problem to date. Therefore, this paper aims to deal with non-uniform illumination problems for bridge coating assessment and to achieve automated rust intensity recognition. This paper starts with selection of the best color configuration for non-uniformly illuminated rust image segmentation. The adaptive-network-based fuzzy inference system (ANFIS) is adopted as the framework to develop the new model, the box-and-ellipse-based neuro-fuzzy approach (BENFA). Finally, the performance of BENFA is compared to the Fuzzy C-Means (FCM) method, which is often used in image recognition, to show the advantage and robustness of BENFA.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.

• 조명전기설비학회논문지
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• 제17권4호
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• pp.57-63
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• 2003

가공 송전선로의 아연도금 강심 알루미늄연선 도체들은 장기간 동안 대기오염에 의해 서서히 열화되었기 때문에, 2천년 대에 이르러 수많은 도체들이 예상된 유효수명을 초과하였다. 대부분의 도체들은 경제적인 운용 측면에서 현재 상태들을 평가하지 않으면 안되므로, 이 논문에서는 경년, 환경지표, 및 도체구조와 같은 중요 파라미터들을 사용하여 노화도체의 현재 상태를 평가하기 위한 방법을 제안하였다. 노화도체의 수명에 대응하는 열화등급을 예측하기 위한 진단 방법을 기술하였으며, 이 시스템은 전문가 지식과 경험을 토대로 적응 뉴로퍼지 추론시스템 (Adaptive Neuro-Fuzzy Inference System)으로 설계하였다. 이 진단시스템을 국내의 송전선로에 적용하여, 이 시스템이 노화 ACSR 도체를 비파괴적으로 진단하고 경제적으로 운용하기 위한 방안으로서 효과적으로 사용될 수 있음을 밝혔다.