Optimal architecture artificial neural network model design with exploitative alpha gray wolf optimization for soft calibration of CO concentration measurements in electronic nose applications

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dc.authoridAlagoz, Baris Baykant/0000-0001-5238-6433
dc.authorwosidAlagoz, Baris Baykant/ABG-8526-2020
dc.contributor.authorSimsek, Ozlem Imik
dc.contributor.authorAlagoz, Baris Baykant
dc.date.accessioned2024-08-04T20:53:02Z
dc.date.available2024-08-04T20:53:02Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe low-cost and small size solid-state sensor arrays are suitable to implement a wide-area electronic nose (e-nose) for real-time air quality monitoring. However, accuracy of these low-cost sensors is not adequate for precise measurements of pollutant concentrations. Artificial neural network (ANN) estimation models are used for the soft calibration of low-cost sensor array measurements and significantly improve the accuracy of low-cost multi-sensor measurements. However, optimality of neural architecture affects the performance of ANN estimation models, and optimization of the ANN architecture for a training data set is essential to improve data-driven modeling performance of ANNs to reach optimal neural complexity and improved generalization. In this study, an optimal architecture ANN estimator design scheme is suggested to improve the estimation performance of ANN models for e-nose applications. To this end, a gray wolf optimization (GWO) algorithm is modified, and an exploitative alpha gray wolf optimization (EA-GWO) algorithm is suggested. This modification enhances local exploitation skill of the best alpha gray wolf search agent, and thus allows the fine-tuning of ANN architectures by minimizing a multi-objective cost function that implements mean error search policy. Experimental study demonstrates the effectiveness of optimal architecture ANN models to estimate CO concentration from the low-cost multi-sensor data.en_US
dc.identifier.doi10.1177/01423312221119648
dc.identifier.endpage699en_US
dc.identifier.issn0142-3312
dc.identifier.issn1477-0369
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85139032082en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage686en_US
dc.identifier.urihttps://doi.org/10.1177/01423312221119648
dc.identifier.urihttps://hdl.handle.net/11616/100920
dc.identifier.volume45en_US
dc.identifier.wosWOS:000860128100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofTransactions of The Institute of Measurement and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputational intelligenceen_US
dc.subjectevolutionary algorithmsen_US
dc.subjectcomputationen_US
dc.subjectintelligent processingen_US
dc.subjectmeasurement chemicalen_US
dc.subjectneural networken_US
dc.titleOptimal architecture artificial neural network model design with exploitative alpha gray wolf optimization for soft calibration of CO concentration measurements in electronic nose applicationsen_US
dc.typeArticleen_US

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