대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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- Pages.349-351
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- 2004
두개의 Extended Kalman Filter를 이용한 Recurrent Neural Network 학습 알고리듬
A Learning Algorithm for a Recurrent Neural Network Base on Dual Extended Kalman Filter
- 발행 : 2004.11.12
초록
The classical dynamic backpropagation learning algorithm has the problems of learning speed and the determine of learning parameter. The Extend Kalman Filter(EKF) is used effectively for a state estimation method for a non linear dynamic system. This paper presents a learning algorithm using Dual Extended Kalman Filter(DEKF) for Fully Recurrent Neural Network(FRNN). This DEKF learning algorithm gives the minimum variance estimate of the weights and the hidden outputs. The proposed DEKF learning algorithm is applied to the system identification of a nonlinear SISO system and compared with dynamic backpropagation learning algorithm.