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Indoor air quality control using backpropagated neural networks

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dc.contributor.authorUskenbayeva R.en_US
dc.contributor.authorAltayeva A.en_US
dc.contributor.authorGusmanova F.en_US
dc.contributor.authorAbdulkarimova G.en_US
dc.contributor.authorBerkimbaeva S.en_US
dc.contributor.authorDalbekova K.en_US
dc.contributor.authorSuiman A.en_US
dc.contributor.authorZhanseitova A.en_US
dc.contributor.authorAmreyeva A.en_US
dc.contributor.authorid55623134100en_US
dc.contributor.authorid56128042000en_US
dc.contributor.authorid57207999955en_US
dc.contributor.authorid57207999329en_US
dc.contributor.authorid57209039051en_US
dc.contributor.authorid57223432492en_US
dc.contributor.authorid57279137800en_US
dc.contributor.authorid57279351800en_US
dc.contributor.authorid57279137900en_US
dc.date.accessioned2023-05-29T09:42:25Z
dc.date.available2023-05-29T09:42:25Z
dc.date.issued2022
dc.descriptionAir conditioning; Air quality; Backpropagation; Indoor air pollution; Neural networks; Proportional control systems; Quality control; Two term control systems; Ventilation; Air quality control; Back-propagated neural networks; Back-propagation neural networks; Fast response; Indoor air; Indoor air quality; Math model; PID; Proportional integral derivatives; Residential construction; Controllersen_US
dc.description.abstractProviding comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying the proposed system. The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room. The proposed developed model takes into consideration international Heating, Refrigerating, and air conditioning standards as ASHRAE AND ISO. Based on the findings, we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy. � 2022 Tech Science Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.32604/cmc.2022.020491
dc.identifier.epage3853
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85116011920
dc.identifier.spage3837
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116011920&doi=10.32604%2fcmc.2022.020491&partnerID=40&md5=09d4448181e88e629e612aa1aece44c2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27304
dc.identifier.volume70
dc.publisherTech Science Pressen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleComputers, Materials and Continua
dc.titleIndoor air quality control using backpropagated neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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