Title :
Design of a neural-based A/D converter using modified Hopfield network
Author :
Lee, Bang W. ; Sheu, Bing J.
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
fDate :
8/1/1989 12:00:00 AM
Abstract :
The architecture associated with the Hopfield network can be utilized in the VLSI realization of several important engineering optimization functions for signal processing purposes. The properties of local minima in the energy function of Hopfield networks are investigated. A design technique to eliminate these local minima in the Hopfield neural-based analog-to-digital converter has been developed. Experimental data agree well with theoretical results in the output characteristics of the neural-based data converter
Keywords :
VLSI; analogue-digital conversion; neural nets; VLSI realization; analog-to-digital converter; energy function; engineering optimization functions; local minima; modified Hopfield network; neural-based A/D converter; output characteristics; signal processing; Artificial neural networks; Associative memory; Circuits; Computer networks; Hopfield neural networks; Neural networks; Power engineering and energy; Resistors; Signal processing; Very large scale integration;
Journal_Title :
Solid-State Circuits, IEEE Journal of
