An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications

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dc.authoridAlimohammadi, Hossein/0000-0002-8867-8988
dc.authoridVassiljeva, Kristina/0000-0002-4178-1267
dc.authoridAri, Davut/0000-0001-6439-7957
dc.authoridAlagoz, Baris Baykant/0000-0001-5238-6433
dc.authoridTepljakov, Aleksei/0000-0002-7158-8484
dc.authoridIMIK SIMSEK, OZLEM/0000-0002-4192-0255
dc.authoridPetlenkov, Eduard/0000-0003-2167-6280
dc.authorwosidAlimohammadi, Hossein/AAX-5165-2020
dc.authorwosidVassiljeva, Kristina/AAC-8226-2021
dc.authorwosidAri, Davut/GPX-1182-2022
dc.authorwosidAlagoz, Baris Baykant/ABG-8526-2020
dc.authorwosidTepljakov, Aleksei/F-1632-2017
dc.authorwosidPetlenkov, Eduard/G-5537-2017
dc.contributor.authorAlagoz, Baris Baykant
dc.contributor.authorSimsek, Ozlem Imik
dc.contributor.authorAri, Davut
dc.contributor.authorTepljakov, Aleksei
dc.contributor.authorPetlenkov, Eduard
dc.contributor.authorAlimohammadi, Hossein
dc.date.accessioned2024-08-04T20:51:57Z
dc.date.available2024-08-04T20:51:57Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractNeuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.en_US
dc.description.sponsorshipEstonian Research Council [PRG658]en_US
dc.description.sponsorshipThis work was partially supported by the Estonian Research Council under Grant PRG658.en_US
dc.identifier.doi10.3390/s22103836
dc.identifier.issn1424-8220
dc.identifier.issue10en_US
dc.identifier.pmid35632245en_US
dc.identifier.scopus2-s2.0-85130267279en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/s22103836
dc.identifier.urihttps://hdl.handle.net/11616/100657
dc.identifier.volume22en_US
dc.identifier.wosWOS:000801755200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectneuroevolutionen_US
dc.subjectevolutionary optimizationen_US
dc.subjectmultiplicative neuron modelen_US
dc.subjectconcentration estimationen_US
dc.subjectelectronic noseen_US
dc.subjectIndustry 4en_US
dc.subject0en_US
dc.titleAn Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applicationsen_US
dc.typeArticleen_US

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