Enhanced cuckoo search algorithm for industrial winding process modeling

Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industri...

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Veröffentlicht in:	Journal of intelligent manufacturing Jg. 34; H. 4; S. 1911 - 1940
Hauptverfasser:	Braik, Malik, Sheta, Alaa, Al-Hiary, Heba, Aljahdali, Sultan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2023 Springer Nature B.V
Schlagworte:	Advanced manufacturing technologies Algorithms Business and Management Constraint modelling Control Control systems Machines Manufacturing Mechatronics Nonlinear systems Parameter estimation Processes Production Robotics Search algorithms Winding Industrial winding process Cuckoo search algorithm Nonlinear model Linear model
ISSN:	0956-5515, 1572-8145
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Abstract	Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods.
AbstractList	Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods.
Author	Aljahdali, Sultan Sheta, Alaa Braik, Malik Al-Hiary, Heba
Author_xml	– sequence: 1 givenname: Malik surname: Braik fullname: Braik, Malik organization: Al-Balqa Applied University – sequence: 2 givenname: Alaa orcidid: 0000-0002-3727-6276 surname: Sheta fullname: Sheta, Alaa email: shetaa1@southernct.edu organization: Southern Connecticut State University – sequence: 3 givenname: Heba surname: Al-Hiary fullname: Al-Hiary, Heba organization: Al-Balqa Applied University – sequence: 4 givenname: Sultan surname: Aljahdali fullname: Aljahdali, Sultan organization: Taif University
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Copyright	This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022.
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SubjectTerms	Advanced manufacturing technologies Algorithms Business and Management Constraint modelling Control Control systems Machines Manufacturing Mechatronics Nonlinear systems Parameter estimation Processes Production Robotics Search algorithms Winding
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Title	Enhanced cuckoo search algorithm for industrial winding process modeling
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