Boundedness of estimator with an improved back propagation algorithm

This paper proposes a novel learning algorithm with a variable learning gain and a /spl sigma/-modification term for a multilayered neural network. The learning gain is decided by the 'Levenberg-Marquardt' algorithm, which is a nonlinear least squares method. The boundedness of the weighti...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the 1996 IEEE IECON : 22nd International Conference on Industrial Electronics, Control, and Instrumentation Ročník 1; s. 309 - 314 vol.1
Hlavní autoři: Ikeda, K., Shin, S.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 1996
Témata:
Adaptive control
Education
Filters
Gain
Multi-layer neural network
Neural networks
Neurons
Physics
Robust control
Upper bound
ISBN:0780327756, 9780780327757
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes a novel learning algorithm with a variable learning gain and a /spl sigma/-modification term for a multilayered neural network. The learning gain is decided by the 'Levenberg-Marquardt' algorithm, which is a nonlinear least squares method. The boundedness of the weightings is shown from a viewpoint of the robust adaptive control theory and a relationship between data sizes and learning rates is considered. Furthermore, simple numerical simulations are presented to show the efficiency of the proposed learning algorithm.
ISBN:0780327756
9780780327757
DOI:10.1109/IECON.1996.570970