Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm

Wen Yu; José De Jesús Rubio; Xiaoou Li

doi:10.1109/IJCNN.2005.1555937

Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

4 Citas (Scopus)

Resumen

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Páginas	700-705
Número de páginas	6
DOI	https://doi.org/10.1109/IJCNN.2005.1555937
Estado	Publicada - 2005
Publicado de forma externa	Sí
Evento	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canadá Duración: 31 jul. 2005 → 4 ago. 2005

Serie de la publicación

Nombre	Proceedings of the International Joint Conference on Neural Networks
Volumen	2

Conferencia

Conferencia	International Joint Conference on Neural Networks, IJCNN 2005
País/Territorio	Canadá
Ciudad	Montreal, QC
Período	31/07/05 → 4/08/05

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10.1109/IJCNN.2005.1555937

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Yu, W., De Jesús Rubio, J., & Li, X. (2005). Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm. En Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 700-705). Artículo 1555937 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2). https://doi.org/10.1109/IJCNN.2005.1555937

@inproceedings{ed7cfa8a34dd4cbe8e838b447cc44b91,

title = "Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm",

abstract = "Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.",

author = "Wen Yu and {De Jes{\'u}s Rubio}, Jos{\'e} and Xiaoou Li",

year = "2005",

doi = "10.1109/IJCNN.2005.1555937",

language = "Ingl{\'e}s",

isbn = "0780390482",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "700--705",

booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005",

note = "International Joint Conference on Neural Networks, IJCNN 2005 ; Conference date: 31-07-2005 Through 04-08-2005",

}

Yu, W, De Jesús Rubio, J & Li, X 2005, Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm. En Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1555937, Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 700-705, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canadá, 31/07/05. https://doi.org/10.1109/IJCNN.2005.1555937

Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm. / Yu, Wen; De Jesús Rubio, José; Li, Xiaoou.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 700-705 1555937 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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N2 - Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.

AB - Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.

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ER -

Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm

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