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...

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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
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