___

1.Civelek, Ö., Dairesel Plakların Nöro- fuzzy tekniği ile Analizi, DEÜ Müh. Fak. Fen ve Müh. Dergisi, 1-2,13-31,1999.

2.Ataman, F., Kaynak, T., Yüncü, S., Bilgisayar ortamında sistem modelleme yoluyla yapay zeka içeren çözümlerin irdelenmesi, Elektrik-Elektronik-Bilgi sayar Mühendisliği 8. Ulusal Kongresi, 677-679,Gaziantep, 1998.

3.Demir, Y., Tuntaş, R., Köksal, M., Anahtarlamalı devrelerin yapay sinir ağları ile analizi, Elektrik-Elektronik-Bilgisayar Mühendisliği 8. Ulusal Kongresi, 673-679, Gaziantep, 1998.

4.Soteris, A. K., Bojic, M., Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy, 25 ,479-491, 2000.

5.Üçgül, İ., Akarslan, F., Şencan, A., Dokuma Kumaşların Kuruma Hızı Değerlerinin Yapay Sinir Ağları Metodu İle Tahmini, Isı Bilimi ve Tekniği Dergisi,23,1-7,2003.

6.Shengwei, W., Jin, X., Model-based optimal control of VAV air-conditioning system using genetic algorithm, Building and Environment, 35, 471-487,2000.

7.Kalogirou, A.S., Applications of artificial neural networks in energy systems a review, Energy Conversion and Management, 40, 1073-1087, 1999.

8.Kalogirou, A.S., Artificial neural networks in renewable energy systems applications: a review, Renewable & Sustainable Energy Reviews, 5, 373-401, 2001.

9.Swider, D.J., Browne, M.W., Bansal, P.K., Kecman,V., Modelling of vapour compression liquid chillers with neural networks, Applied Thermal Energy, 21, 311-329, 2001.

10.Chow, T.T., Zhang, G.Q, Lin, Z., Song, C.L., Global optimization of absorbtion chiller system by genetic algorithm and neural network, Energy and Buildings, 34, 103,2002.

11.İslamoğlu, Y, Kurt, A., Heat Transfer Analysis Using ANNs With Experimental Data for Air Flowing Corrugated Channels, I. J. Of Heat And Mass Transfer, 47, 1361-1365, 2004.

12.Sözen, A., Arcaklıoğlu, E., Solar potential in Turkey, Applied Energy, 80, 35-45, 2004.

13.Tanyolu, T., A Neural Network Approach with Self-Organized Principal Component Analsis for identification of Dehumudifying Coils, ASHRAE Trans., 1999.

14. Hamdy, K. Elminir, Faiz F., Areed, Tarek S. Elsayed, Estimation of solar radiation components incident on Helwan site using neural Networks, Solar Energy, 79, 270-279, 2005.

15.Mellit,A.,Benghanem,M.,Hadj,A.,Arab,A.,,Guessoum, A., A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach, Solar Energy 79,469-482, 2005.

16.Alam, S., Kaushik, S.C., Garg, S.N., Computation of beam solar radiation at normal incidence tlsing artificial neural network, Renewable Energy,2005.

17.Tymvios, F.S., Jacovides, C.P., Michaelides, S.C.,Scouteli, C., Comperative study of Angstrom's and artificial neural networks methodologies in estimating global solar radiation, Solar Energy, 78,752-762, 2005.

18.Ertunç, H.M., Hosoz, M., Artificial neural network analysis of a refrigeration system with an evaporative condenser, Applied Thermal Energy, 26, 627-635,2006.

19.Şencan, A., Yakut, A.K., Kalogirou, S.A., Thermodinamic analysis of absorbtion systems using artificial neural network, Renewable Energy, 31-, 29 43,2006.

20.Hosoz, M., Ertunç, H.M., Artificial neural network analysis of an automobile air conditioning system, Energy Conversion and Management, 2005.

21.Shiming, D., The Application of feedforward control in a direct expansion (DX) air conditioning plant, Building and Environment, 37, 35, 2002.

22.Kanarachos, A., Geramanis, K., Multivariable control of single zone hydronik heating system with neural networks, Energy Convers., 39-13,1317-1335,1998.

23.Karakuzu, C., Öztürk, S., Yapay sinir ağları ile bir kontrol uygulaması, Elektrik-Elektronik-Bilgisayar Mühendisliği 8. Ulusal Kongresi, 689-692, Gaziantep, 1998.

24.Yang, I., Yeo, M.S., Kim, K.W., Application of artificial neural network to predict the optimal start time for heating system in building, Energy Conversion and Management, 44, 2791-2809, 2003.