DocumentCode :
356068
Title :
Maximum/minimum signal selector for competitive learning neural network
Author :
Abdel-Aty-Zohdy, Hoda S. ; El-Licy, Fatma A.
Author_Institution :
Dept. of Electr. & Syst. Eng., Oakland Univ., Rochester, MI, USA
Volume :
1
fYear :
1999
fDate :
1999
Firstpage :
392
Abstract :
A sixteen-input maximum/minimum selector circuit is designed, based on multi-input current comparators, implemented in MOSIS 2 um CMOS, n-well technology. It operates with input-signal range 1.6-to-3.6 V, with a 5 V supply. The circuit is developed for possible applications in competitive learning neural networks (NNs) for sensors output interface: (1) as a preprocessor; for crude classification of sensor outputs-the circuit operates as a maximum value selector to compute the infinity norm of the distance between the input features and corresponding neural synaptic strength; (2) as a post processor, minimum distance value selector to determine the competitive winning neuron. Experimental measurements have been favorable
Keywords :
CMOS analogue integrated circuits; analogue processing circuits; current comparators; neural chips; unsupervised learning; 1.6 to 3.6 V; 2 micron; CMOS; competitive learning neural network; competitive winning neuron; infinity norm; input features; maximum/minimum signal selector; multi-input current comparators; n-well technology; neural synaptic strength; sensor outputs; Analog circuits; Artificial intelligence; CMOS technology; Design engineering; H infinity control; Laboratories; Microelectronics; Neural networks; Neurons; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1999. 42nd Midwest Symposium on
Conference_Location :
Las Cruces, NM
Print_ISBN :
0-7803-5491-5
Type :
conf
DOI :
10.1109/MWSCAS.1999.867288
Filename :
867288
Link To Document :
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