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
- 2013.5
- 수록면
- 157 - 162 (6page)
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초록· 키워드
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Control of nonlinear systems require some explicit representation of the nominal plant dynamics. However, for highly complex systems obtaining such mathematical form may not be possible. In these situations, researchers have applied online learning techniques in order to achieve the control objective. Echo state networks (ESN), that are a class of recurrent neural networks, have provided promising results in this online learning area. However, the online learning of ESNs has been restricted to using recursive least squares (RLS) which has its own drawbacks such as numerical instability due to round-off errors, slow tracking capability for time-varying parameters, and high sensitivity to initial conditions of the algorithm. In this work, we investigate various algorithms that can be used for the online training of ESNs. The algorithms are evaluated numerically based on the tracking performance for a given reference trajectory and their computational complexity.
목차
ABSTRACT
INTRODUCTION
ECHO STATE NETWORKS FOR ONLINE LEARNING CONTROL
REGRESSION METHODS FOR ONLINE LEARNING
EXPERIMENTS BASED ON NUMERICAL SIMULATIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-400-002404532