Open AccessSYNTHESIS AND FPGA–IMPLEMENTATION BASED NEURAL TECHNIQUE OF A NONLINEAR ADC MODEL
Author(s) -
Mounir Bouhedda,
Mokhtar Attari
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.4.1.321
Subject(s) - vhdl , field programmable gate array , computer science , artificial neural network , hardware description language , nonlinear system , computer architecture , field (mathematics) , gate array , computer hardware , computer engineering , electronic engineering , artificial intelligence , engineering , mathematics , physics , quantum mechanics , pure mathematics
The aim of this paper is to introduce a new architecture using Artificial Neural Networks (ANN) in designing a 6-bit nonlinear Analog to Digital Converter (ADC). A study was conducted to synthesise an optimal ANN in view to FPGA (Field Programmable Gate Array) implementation using Very High-speed Integrated Circuit Hardware Description Language (VHDL). Simulation and tests results are carried out to show the efficiency of the designed ANN.