On the choice of parameters of the cost function in nested modular RNN's

We address the choice of the coefficients in the cost function of a modular nested recurrent neural-network (RNN) architecture, known as the pipelined recurrent neural network (PRNN). Such a network can cope with the problem of vanishing gradient, experienced in prediction with RNN’s. Constraints on...

Full description

Saved in:
Bibliographic Details
Main Authors: Danilo P. Mandic, Jonathon Chambers
Format: Default Article
Published: 2000
Subjects:
Mechanical engineering not elsewhere classified
Cost function
Forgetting factor
Nesting
Pipelined recurrent neural networks
Recurrent neural networks
Mechanical Engineering not elsewhere classified
Online Access:https://hdl.handle.net/2134/5796
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://hdl.handle.net/2134/5796

Similar Items