• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.102.1-102
• /
• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.56-60
• /
• 1992

This paper addresses the problem of designing a neural network based controller for a discrete-time nonlinear dynamical system. Using two multi-layered neural networks we first design an indirect controller the weights of which are updated by the informations obtained from system identification. The weight update is executed by parameter optimization method under Lagrangian formulation. For the nonlinear dynamical system, we define several cost functions and by computer simulations analyze the control performances of them and the effects of penalty-weighting values.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.10
• /
• pp.123-129
• /
• 1994

The segmentation of range image is essential to recognize the three dimensional object. Generally, surface curvature is well-known feature for segmentation and classification of the fange image, but it is sensitive to noies. In this paper, we propose the structure of hierarchical neural network using surface curvature for segmentation of range images. The hierarchical structure of neural networks is robust to noise and the result of segmentaion is better than conventional optimization method of single level.

• Proceedings of the KSR Conference
• /
• 2000.11a
• /
• pp.720-726
• /
• 2000

The advance of traction motor control technology and the complexity of the traction power supply system makes the simulation essential in determining the dimension of the traction power supply system. The conventional method, use of the simplified and/or empirical equations, becomes inadequate in optimization of the design. The simulator presented in this paper is a numerical time based simulator running on a PC. The input to the simulator includes the track data, the train characteristic, network data and operating data. Basically the simulator conducts train running simulation and loadflow study repeatedly. The principle algorithms and its output is discussed in the paper.

• Proceedings of the KIEE Conference
• /
• 1990.07a
• /
• pp.492-495
• /
• 1990

This paper introduces a path planning technique for a mobile robot in the presence of obstacles. In the technique, workspace is described by regional graph and represented obstacles by the three-layer neural network. And performance cost is defined under consideration both the traveling distance and the safety of a mobile robot. Then a collision-free path is obtained using the neural optimization technique.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.05a
• /
• pp.216-216
• /
• 2004

본 논문은 우리가 일상 생활에서 접하는 에어컨의 핵심 부품인 열 교환기의 제작과정 중에서 확관 공정에서의 확관속도 최적화에 관한 것이다 여기서 열 교환기는 구멍 뚫린 박판형태의 방열핀과 이 구멍을 통과하는 구리재질의 관인 헤어핀의 2가지 주요 부품으로 구성되어있다 그리고 확관기(Fig. 1)에 있어서의 확관공정은 Fig. 2에서 보는 바와 같이 소성변형을 통한 관의 반지름 방향의 팽창으로 방열핀과 헤어핀을 결합시켜주는 높은 정밀도를 요구하는 작업이다.(중략)

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2749-2751
• /
• 2002

일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마을 분류하는 새로운 알고리즘을 보고한다. 데이터분포를 통계적인 평균치와 표준편차를 이용하여 특징지었으며, 바이어스 인자을 이용하여 9 종류의 데이터을 발생하였다. 각 데이터에 대하여 GRNN의 학습인자를 최적화하였으며, 모델성능은 예측과 분류 정확도로 나누어 바이어스와 학습인자의 함수로 분석하였다. 바이어스는 모델성능에 상당한 영향을 주었으며, 학습인자와의 상호작용을 통하여 완전 분류를 이루었다.

• Proceedings of the KSR Conference
• /
• 2002.10b
• /
• pp.989-994
• /
• 2002

In case the fault occurs in railway high voltage power supply network, protective relaying system must selectively detect 1 wire ground fault of grounding or non-grounding system. And this study presents railway high voltage power supply system model using PSCAD/EMTDC Vet 3.08 for circuit analysis and fault studies. In this paper, we propose protection method of non-grounding system of railway high voltage power supply system. The result shows its usefulness.

• Proceedings of the Korea Database Society Conference
• /
• 1995.12a
• /
• pp.3-19
• /
• 1995

시스템 Technology ★ Server Technology - 운영환경구축 ★ Network 구성설계 - ATM, FDDI, NMS ★ Client/Server시스템 구성별 Bench Marking ★ Windows 메뉴 및 GUI 설계 ★다기능 PC 운영환경 설정 시스템 Technology ★ Data Base Technology - DB Administration - BB Performance Tuning ★ System Integration Technology - Application Integration - System Flow Control - Task Control - Applicational Interface - S/W Down Load 시스템 Technology ★ Memory Optimization ★ IBM/Facom Host API ★ 영상전화 Customizing - Intel Proshare ★ Auto Dialing - CTI Link ★ IC-Card Interface 시스템 Technology ★ Sound 처리 - Voice Mail - 음절 처리 ★ Image 처리 ★도움말 처리 - Hyper Text 시스템 Technology ★ Socket Programming - 긴급메일 - Peer to peer message switching ★ Set Up Programming -Install Shield ★ DB Access Programming - DB-Library ★ TCP/IP Programming(중략)

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.374-378
• /
• 1998

신경망은 복잡하게 나타나는 비선형성을 가지는 실제의 다양한 문제들에 적용이 가능할 뿐만 아니라, 정보들이 가중치에 분산되어 저장됨으로서 강인성을 가지고 있다. 그러나 전방향 다층 신경망 구조를 학습할 수 있는 역전파 알고리즘은 초기 가중치의 영향에 의하여 학습된 결과가 지역 최소점에 빠지기 쉬운 경향이 있다. 본 논문에서는 이러한 문제점을 해결하기 위한 한가지 방법으로서 유전자 알고리즘을 이용하여 전방향 다층 신경망의 가중치를 학습하여, 지역 최소점에 빠지지 않고 학습이 이루어짐을 보인다.