Dergiler / Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2010 / Cilt: 1 Sayı: 2

Bulanık mantık ve PIC kullanılarak bir klima sisteminin kontrolü

Control of an air conditional system with fuzzy logic and PIC using

Sayfa
167–180
Yayın Tarihi
2010-12-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Heater and cooler fans work as required climate.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this study an air conditioner system was put into practice as programming PIC by fuzzy logic system.
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Temel Bulgular & Veri:The system keeps temperature of atmosphere between 19-23oC.
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Sonuç & Katkı:Heater and cooler fans work as required climate.
🔬Tür:Makale
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Fuzzy logic · climate · air conditional

Özet

In this study an air conditioner system was put into practice as programming PIC by fuzzy logic system. The system keeps temperature of atmosphere between 19-23oC. As input variable damp and heat values are taken by sensor called SHT11 and they are transmitted to PIC 16F876 which programmed by fuzzy logic system. Heater and cooler fans work as required climate.

Abstract

In this study an air conditioner system was put into practice as programming PIC by fuzzy logic system. The system keeps temperature of atmosphere between 19-23oC. As input variable damp and heat values are taken by sensor called SHT11 and they are transmitted to PIC 16F876 which programmed by fuzzy logic system. Heater and cooler fans work as required climate.

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Bu Makaleye Atıf Yap

Erkaymaz, H., Çayıroğlu, İ. (2010). Bulanık mantık ve PIC kullanılarak bir klima sisteminin kontrolü. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 1 Sayı: 2, 167–180. https://ajindex.com/articles/1527880

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Kaynakça (13)

  1. Elmas, Ç. (2003). Bulanık Mantık Denetleyiciler (Kuram, Uygulama, Sinirsel Bulanık Mantık), Seçkin Yayıncılık, pp. 230, ISBN: 9753476132, Ankara.
  2. Özek, A., & Sinecen, M. (2004). Klima Sistem Kontrolünün Bulanık Mantıkla Modellemesi, Pamukkale Ünv. Müh. Fak. Mühendislik Bilimleri Dergisi, 10, 353-358.
  3. Egilegor, B., & Uribe, J. P. (1997). A Fuzzy Control Adapted By A Neural Network To Maintain A Dwelling Within Thermal Comfort, IBPSA-International Building Performance Simulation Association, Prague, Czech Republic, September 8-10.
  4. Lah, M. T., Zupanc, B., & Krainer, A. (2004). Fuzzy control for the illumination and temperature comfort in a test chamber. Building and Environment, 40, 1626–1637.
  5. Dounis, A. I., Manolakis, D. E. (2000). Design Of a Fuzzy Sysytem For Living Space Termal – Comfort Regulation, Applied Energy, 69, 119-144.
  6. Chena, K., Rysb, M. J., & Leeb, E. S., (2005). Modeling of thermal comfort in air conditioned rooms by fuzzy regression analysis, Mathematical and Computer Modelling, 43, 809– 819.
  7. Duan, Y. (1999). Fuzzy Role Based Expert System for Human Thermoregulation Model, University Of California At Berkeley, Mechanical Engineering, Final Project.
  8. Michel, J. B., & El-khoury, M. (2006). Design and Optimization of a Fuzzy Controller for Heat and Ventilation in Buildings, http://www.csem.ch
  9. Zupančič, B., Škrjanc, I., Krainer, A., & Furlan, B. (2005). Advanced Fuzzy Control Of The Temperature In The Test Chamber, http://wire.ises.org
  10. Şenol, F. (2000), Gazi Üniversitesi Teknik Eğitim Fakültesi Elektronik Bilgisayar Eğitimi Bölümü, Bitirme tezi.
  11. Özek, A., & Sinecan, M. (2003), Klima Sistem Kontrolünün Bulanik Mantık İle Modellemesi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 10, 353-358.
  12. Feddern, J., & Gebhardt, J. (1995). Fuzzy Logic’le Klima Kontrolü, ELO Elektronik Dergisi, 91, 43-51.
  13. Elmas, Ç. (2003). Bulanık Mantık Denetleyiciler (Kuram, Uygulama, Sinirsel Bulanık Mantık), Seçkin Yayıncılık, pp. 230, ISBN: 9753476132, Ankara.