DocumentCode :
3240166
Title :
An asymmetric neural network successive approximation A/D converter
Author :
Huang, Heng ; Siy, P. ; Liu, Guo-Ping ; Polis, Michael
Author_Institution :
Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA
fYear :
1989
fDate :
0-0 1989
Abstract :
Summary form only given, as follows. Analog-to-digital (A/D) converters have enabled various analog signals to be processed by today´s flexible, programmable, fast, and accurate digital devices such as digital computers. There are several existing designs of digital A/D converters, such as the successive-approximation A/D converter and the symmetric neural network A/D converter which was introduced by Hopfield and Tank. A novel A/D converter using an asymmetric neural network, which shows simple structure and advantages in conversion speed and accuracy over the other A/D converters, has been developed.<>
Keywords :
analogue-digital conversion; neural nets; accuracy; asymmetric neural network successive approximation A/D converter; conversion speed; digital A/D converters; digital devices; Analog-digital conversion; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
Type :
conf
DOI :
10.1109/IJCNN.1989.118334
Filename :
118334
Link To Document :
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