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    Indoor Air Quality Control Using Backpropagated Neural Networks

    Raissa Uskenbayeva1, Aigerim Altayeva1,*, Faryda Gusmanova2, Gluyssya Abdulkarimova3, Saule Berkimbaeva4, Kuralay Dalbekova4, Azizah Suiman5, Akzhunis Zhanseitova6, Aliya Amreyeva2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3837-3853, 2022, DOI:10.32604/cmc.2022.020491

    Abstract Providing 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… More >

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