지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.10
- 수록면
- 78 - 83 (6page)
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초록· 키워드
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This paper presents a neural network-based control method applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the effectiveness of the neural network controller and its capability to compensate for structural parameter variations.
목차
Abstract
1. INTRODUCTION
2. DYNAMICAL MODEL OF THESTRUCTURE
3. ARMAX MODEL OF THE STRUCTURE
4. GENERALIZED MINIMUM VARIANCE CONTROLLER
5. THE NEURAL NETWORK COTROLLER
6. MATHEMATICAL MODEL OFEARTHQUAKE GROUND MOTION
7. SIMULATION RESULTS
8. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-569-000912108