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A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA Cover

A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA

Journal of Artificial Intelligence and Soft Computing Research
Volume 9 (2019): Issue 1 (January 2019)
By: Igor Aizenberg,  Antonio Luchetta,  Stefano Manetti and  Maria Cristina Piccirilli  
Open Access
|Aug 2018

Authors

Igor Aizenberg

igor.aizenberg@manhattan.edu

Manhattan College, New York

Antonio Luchetta

antonio.luchetta@unifi.it

Dept. of Information Engineering, University of Florence, Firenze, Italy

Stefano Manetti

stefano.manetti@unifi.it

Dept. of Information Engineering, University of Florence, Firenze, Italy

Maria Cristina Piccirilli

mariacristina.piccirilli@unifi.it

Dept. of Information Engineering, University of Florence, Firenze, Italy
DOI: https://doi.org/10.2478/jaiscr-2018-0021
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Language: English
Page range: 5 - 19
Submitted on: May 28, 2017
Accepted on: Oct 19, 2017
Published on: Aug 20, 2018
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Analog circuits,
Complex-valued neural networks,
Lumped models,
Testability
Related subjects:
Computer sciences,
Artificial intelligence,
Databases and data mining

© 2018 Igor Aizenberg, Antonio Luchetta, Stefano Manetti, Maria Cristina Piccirilli, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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