Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. It addresses the limitations of the original Archimedes optimization algorithm (AOA) through two key modifications: rebalancing the explora...

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Bibliographic Details
Main Authors: Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad
Format: Article
Language:English
Published: Elsevier B.V. 2024
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/43973/
http://umpir.ump.edu.my/id/eprint/43973/1/Data-driven%20continuous-time%20Hammerstein%20modeling%20with%20missing%20data.pdf

Internet

http://umpir.ump.edu.my/id/eprint/43973/
http://umpir.ump.edu.my/id/eprint/43973/1/Data-driven%20continuous-time%20Hammerstein%20modeling%20with%20missing%20data.pdf

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