DocumentCode
396175
Title
Recurrent neural networks: overview and perspectives
Author
Michel, Anthony N.
Author_Institution
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Volume
3
fYear
2003
fDate
25-28 May 2003
Abstract
In the implementation of artificial neural networks, several limitations are encountered which may affect their qualitative behavior and performance. These include delays, parameter perturbations and interconnection constraints. Depending on the particular type of implementation, more than one of these limitations will usually be encountered simultaneously. In the present paper, we address these issues.
Keywords
asymptotic stability; delays; interconnections; recurrent neural nets; robust control; sparse matrices; asymptotically stable equilibrium; interconnection constraints; overview; parameter perturbations; qualitative behavior; recurrent neural networks; robust stability; sparse coefficient matrices; time delays; Artificial neural networks; Delay effects; Equations; Intelligent networks; Neural networks; Neurons; Recurrent neural networks; Symmetric matrices; Transportation; Very large scale integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on
Print_ISBN
0-7803-7761-3
Type
conf
DOI
10.1109/ISCAS.2003.1205059
Filename
1205059
Link To Document