DocumentCode
1295097
Title
Training partially recurrent neural networks using evolutionary strategies
Author
Greenwood, Garrison W.
Author_Institution
Dept. of Electr. & Comput. Sci., Western Michigan Univ., Kalamazoo, MI
Volume
5
Issue
2
fYear
1997
fDate
3/1/1997 12:00:00 AM
Firstpage
192
Lastpage
194
Abstract
This correspondence presents the latest results of using evolutionary strategies (ESs) to design partially recurrent neural networks for viseme recognition. ESs are stochastic optimization algorithms based upon the principles of natural selection found in the biological world. Our results indicate that ESs can be effectively used to determine the synaptic weights in neural networks and can outperform backpropagation techniques
Keywords
learning (artificial intelligence); recurrent neural nets; speech recognition; design; evolutionary strategies; partially recurrent neural networks training; stochastic optimization algorithms; synaptic weights; viseme recognition; Auditory system; Backpropagation algorithms; Helium; Lips; Neural networks; Pipeline processing; Recurrent neural networks; Speech recognition; Stochastic processes; Telephony;
fLanguage
English
Journal_Title
Speech and Audio Processing, IEEE Transactions on
Publisher
ieee
ISSN
1063-6676
Type
jour
DOI
10.1109/89.554781
Filename
554781
Link To Document