DocumentCode
3333516
Title
Recursive neural networks for signal processing and control
Author
Hush, D. ; Abdallah, C. ; Horne, B.
Author_Institution
Dept. of Electr. & Comput. Eng., Mexico Univ., Albuquerque, NM, USA
fYear
1991
fDate
30 Sep-1 Oct 1991
Firstpage
523
Lastpage
532
Abstract
The authors describe a special type of dynamic neural network called the recursive neural network (RNN). The RNN is a single-input single-output nonlinear dynamical system with a nonrecursive subnet and two recursive subnets arranged in the configuration shown. The authors describe the architecture of the RNN, present a learning algorithm for the network, and provide some examples of its use
Keywords
neural nets; signal processing; architecture; dynamic neural network; learning algorithm; nonlinear dynamical system; recursive neural network; signal processing; single-input; single-output; Delay; Multilayer perceptrons; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Output feedback; Process control; Recurrent neural networks; Signal processing; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing [1991]., Proceedings of the 1991 IEEE Workshop
Conference_Location
Princeton, NJ
Print_ISBN
0-7803-0118-8
Type
conf
DOI
10.1109/NNSP.1991.239489
Filename
239489
Link To Document