Toward an optimal PRNN-based nonlinear predictor

We present an approach for selecting optimal parameters for the pipelined recurrent neural network (PRNN) in the paradigm of nonlinear and nonstationary signal prediction. We consider the role of nesting, which is inherent to the PRNN architecture. The corresponding number of nested modules needed f...

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Bibliographic Details
Main Authors: Danilo P. Mandic, Jonathon Chambers
Format: Default Article
Published: 1999
Subjects:
Mechanical engineering not elsewhere classified
Forgetting factor
Nesting
Nonlinear prediction
PRNN
RNN
Mechanical Engineering not elsewhere classified
Online Access:https://hdl.handle.net/2134/5798
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https://hdl.handle.net/2134/5798

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