Identification of nonlinear dynamic systems using higher order diagonal recurrent neural network

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A new neural network architecture, called a higher order diagonal recurrent neural network (HDRNN), is presented. The architecture of an HDRNN is a modified model of the diagonal recurrent neural network (DRNN) with one hidden layer which is composed of self-recurrent neurons and additional multiplication inputs between conventional inputs and self-recurrent neurons. The authors derive a generalised dynamic backpropagation algorithm and show that the proposed HDRNN not only gives more accurate identification results, but also requires a shorter training time to obtain the desired accuracy.
Publisher
IEE-INST ELEC ENG
Issue Date
1997-12
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.33, no.25, pp.2133 - 2135

ISSN
0013-5194
URI
http://hdl.handle.net/10203/70016
Appears in Collection
EE-Journal Papers(저널논문)
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