IEICE Trans - Design and Implementation of a Calibrating T-Model Neural-Based A/D Converter

Design and Implementation of a Calibrating T-Model Neural-Based A/D Converter

Zheng TANG
Yuichi SHIRATA
Okihiko ISHIZUKA
Koichi TANNO

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E79-A No.4 pp.553-559
Publication Date: 1996/04/25
Online ISSN:
DOI:
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Analog Signal Processing
Keyword:
neural networks, Hopfield model, calibrating, A/D converter,

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Summary:
A calibrating analog-to digital (A/D) converter employing a T-Model neural network is described. The T-Model neural-based A/D converter architecure is presented with particular emphasis on the elimination of local minimum of the Hopfield neural network. Furthermore, a teacher forcing algorithm is presented and used to synthesize the A/D converter and correct errors of the converter due to offset and device mismatch. An experimental A/D converter using standard 5-µm CMOS discrete IC circuits demonstrates high-performance analog-to-digital conversion and calibrating.