مقایسه توانایی پیش‌بینی نوسان‌های جمعیت سن گندم توسط مدل‌های سیستم استنتاج عصبی- فازی تطبیقی (ANFIS)، روش سطح پاسخ (RSM) و رگرسیون خطی چند متغیره

Arkhipov, M. E., Crueger, M. and Kurtener, D. 2008.Evaluation of ecological conditions using bioindicators: application of fuzzy modeling. Lecture Notes in Computer Science 5072: 491–500.

Azizian, M. S. and Moradi, B. 2012.Study and analyzing the effects of rainfall and drought conditions on the outbreak of Sunn pest in Sanandaj city. The first national agricultural conference in difficult environments. Ramhormaz pp. 10. https://www.civilica.com/Paper-NCAHEC01-NCAHEC01_251.html. (In Farsi)

Balan, B., Mohaghegh, S. and Ameri, S. 1995. State- of- Art- in permeability determination from well log data:Part 1- A comparative study, Model development. Society of Petroleum Enginners 30978: 17-25.

Bianconi, A., Von Zuben, C. J., Serapião, A. B. S. and Govone, J. 2010.Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala. Journal of Insect Science 10: 1-18.

Brown, E. S. and Eralp, M. 1962.The distribution of the species of Eurygaster integriceps in Middle East countries. The Journal of Natural History 5: 63-77.

Buragohain, M. and Mahanta, C. 2008.A novel approach for ANFIS modeling based on full factorial design, Applied Soft Computing 8: 609–625.

Cheng, C. B., Cheng, C. J. and Lee, E. S. 2002.Neuro-fuzzy and genetic algorithm in multiple response optimization, Computers and Mathematics with Applications 44: 1503–1514.

Erahaghi, I., Xuchai, L., Mahnaz, H. and Yusuf, S. 1993.A robust neural network model for pattern recognition of pressure transient test data. Society petroleum engineering annual technical conference and exhibition, 3–6 October 1993. Houston, Texas.

Gorgipour Aftahi, M., Sadeghi, A., Nazemi Rafi, G. and Ghobari, H. 2014.Study of the relationship between density Sunn pest (Eurygaster integriceps Put) in field with moisture (rainfall) after complete loss of wintering places. The First national conference on e-agriculture and sustainable natural resources. Tehran. Arvand Mehr institution of higher education. http://www.civilica.com/Paper-NACONF01-NACONF01_1195.html.

Howe, P. D., Bryant, S. R. and Shreeve, T. G. 2007. Predicting body temperature and activity of adult Polyommatusi carususing neural network models under current and projected climate scenarios. Oecologia 153: 857–869.

Jamali, A., Nariman-Zadeh, N., Darvizeh, A., Masoumi, A. and Hamrang, S. 2009. Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Engineering Applications of Artificial Intelligence 22(4-5): 676-687.

Karimzadeh, R., Hejazi, M. J., Helali, H., Iranipour, Sh. and Mohammadi, A. 2012.The relationship between dynamic population Eurygaster integriceps with environmental variables in East Azarbaijan province. Journal of Plant Protection Sciences 43 (1): 165-177 (In Farsi)

Metin, E. H. and Murat, H. 2008.Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration 31: 1426–1436.

Mittal, G. S, and Zhang, J. 2000. Prediction of temperature and moisture content of frankfurters during thermal processing using neural network. Journal of Applied Poultry Research78(7): 13-24.

Moeini Naghadeh, N. 2002.Regional degree- day forecasting model for predicting developmental stages of sunn pest in the field under variable temperature. Ph.D. thesis. Tarbiat Modares University. Tehran 90 pp.

Mozafari, Gh. and Eghbali Babadi, F. 2013.Analysis of temperature and rainfall characteristics on the downward of sunn pest in Isfahan province. Journal of Lecturer in Humanities - Space Planning and Design 17 (3): 28-44. (In Farsi)

Mozaffari, Gh. A. and Azizian, M. S. 2011.A study of outbreak of sunn pest on the basis of temperature characteristics in Kurdistan province (Case study: Bijar city). Journal of Natural Geography Research 76: 121-135. (In Farsi)

Naderloo, L., Alimardani, R., Omid, M. F., Sarmadian, P., Javadikia, H. and Torabi, M. Y. 2012.Application of ANFIS to predict crop yield based on different energy inputs. Journal of Measurement 45: 1406-1413. (In Farsi)

Pedigo, L. P. and Buntin, G. D. 1993.Handbook of sampling methods for arthropods in agriculture. CRC Press, Boca Raton, FL. 714.

Radjabi, Gh. 2000.Ecology of cereals sunn pests in Iran. Agricultural education publication. Tehran. Iran. 343 pp. (In Farsi)

Radjabi, Gh. 2001. Investigation on the downward migration of hibernating Sunn pest individuals from the altitudes to the cereal fields in Varamin region. Journal of Pests and Plant Diseases68(1): 107-122. (In Farsi)

Radjabi, Gh. 2007.Sunn pest management based on its outbreaks' key factor analysis in Iran. Agricultural Education Publications, Tehran, Iran. 163 pp. (In Farsi)

Richard, A. 2004.Regression Analysis: A Constructive Critique. Sage Publications. Thousand Oaks, CA.

Serge, G. 2001.Designing fuzzy inference systems from data: Interpretability oriented review. IEEE Transaction on Fuzzy Systems 9 (3): 426–442.

Sobhani, B., Salahi, B and Goldoost, A. 2014.Study of dust and evaluation of its prediction based on statistical methods and ANFIS model in Zabol station. Journal of Geography and Development 13 (38): 123-138. (In Farsi)

Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A. and Pokomeda, K. 2014. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Bioresource Technology 160: 150-160.

Worner, S. P. and Gevrey, M. 2006. global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology 43: 858-867.