Analog-to-Digital Conversion using ANNs with Non-Linear Feedback

Analog-to-Digital Conversion using ANNs with Non-Linear Feedback

A Hardware-Oriented Approach

LAP Lambert Academic Publishing ( 2012-10-29 )

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Analog-to-Digital conversion is a basic signal processing task that is needed at various places in the context of modern day mixed-signal systems like instrumentation & control systems, system-on-chip, etc. It is because of the fact that most real-world signals are analog in nature whereas most on-chip computation is digital. The technical literature is replete with electronic implementations of analog to digital converters including, but not limited to, Flash ADC, Successive Approximation ADC, and Sigma-Delta ADC. Given their promise of parallel processing and fast convergence, artificial neural networks have also been employed for analog-to-digital conversion. The first such attempt employed the Hopfield Neural Network and later several variants were introduced. However, most of the existing neural circuits for analog-to-digital conversion have an underlying similarity in the sense that they are derived from the Hopfield Network Architecture. A new scheme for analog-to-digital conversion utilizing a neural circuit for solving systems of linear equations is presented. The circuit employs (2n) opamps and (n+3) resistances for an n bit ADC.

Book Details:

ISBN-13:

978-3-659-28751-0

ISBN-10:

3659287512

EAN:

9783659287510

Book language:

English

By (author) :

Mohd. Samar Ansari
Syed Gulraze Anjum

Number of pages:

84

Published on:

2012-10-29

Category:

Electronics, electro-technology, communications technology