Comparing performances of three CFNN used for DC machine combined parameter and states estimation-Reference-Cited by-同舟云学术

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Comparing performances of three CFNN used for DC machine combined parameter and states estimation

Published:2022-05-06 Issue: Volume: Page:
ISSN:
Container-title:2022 19th International Multi-Conference on Systems, Signals & Devices (SSD)
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Author:

Mellah Hacene¹^ORCID,Hemsas Kamel Eddine²^ORCID,Yahiou Abdelghani¹^ORCID,Cecati Carlo³^ORCID,Sahraoui Hamza⁴^ORCID,Taleb Rachid⁴^ORCID

Affiliation:

1. Université Akli Mohand Oulhadj,Département de génie électriquen,Bouira,Algeria

2. Université Ferhat Abbas Setifl,Département d’électrotechnique,Sétif,Algeria

3. University of L’ Aquila,Department of Information Engineering, Computer Science and Mathematics,L'Aquila,Italy

4. Université Hassiba Benbouali,Département d’ électrotechnique,Chlef,Algeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9955493/9955494/09955868.pdf?arnumber=9955868

Reference48 articles.

1. Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks

2. Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

3. A direct adaptive method for faster backpropagation learning: the RPROP algorithm

4. Target detection through image processing and resilient propagation algorithms

5. Empirical evaluation of the improved Rprop learning algorithms

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