Vol.70, No.2, 2022, pp.3837-3853, doi:10.32604/cmc.2022.020491
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
1 International Information Technology University, Almaty, Kazakhstan
2 Al-Farabi Kazakh National University, Almaty, Kazakhstan
3 Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
4 University of International Business, Almaty, Kazakhstan
5 College of Computing & Informatics, Tenaga National University, Kuala Lumpur, Malaysia
6 L. N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan
* Corresponding Author: Aigerim Altayeva. Email:
Received 26 May 2021; Accepted 12 July 2021; Issue published 27 September 2021
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 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.
Air quality; indoor air; PID; backpropagation; math model; controller
Cite This Article
Uskenbayeva, R., Altayeva, A., Gusmanova, F., Abdulkarimova, G., Berkimbaeva, S. et al. (2022). Indoor Air Quality Control Using Backpropagated Neural Networks. CMC-Computers, Materials & Continua, 70(2), 3837–3853.
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