پیشبینی وزن گوسفند مغانی در طول دوره رشد با استفاده از شبکه‌ عصبی مصنوعی

Bahreini Behzadi, M.R. (2015). Comparison of different growth models and artificial neural network to fit the growth curve of Lori-Bakhtiari sheep. Journal of Ruminant Research, 3(2): 123-146. (In Persian)

Bahraini Behzadi, M.R. (2018). Genetic and phenotypic investigation of growth curve characteristics and prediction of some carcass traits by artificial neural network in Baluchi sheep. PhD thesis, Ferdowsi University of Mashhad. (In Persian)

Beiranvand, F., Beigi Nasiri, MT, Masoudi, A. & Shabaninejad, A. (2016). Study of Lori growth traits using nonlinear models and artificial neural network optimized by genetic algorithm. Animal Science Research Journal, 1: 129-142.

Daskiran, I., Koncagul, S. & Bingol, M. (2010). Growth characteristics of Indigenous Norduz female and male lambs. Journal of Agriculture Science, 16 (1): 62–9.

Fitzhugh, HA. (1976). Analysis of growth curves and strategies for altering their shape. Animal Sciense, 42:1036–1051.

Ganesan, R., Dhanavanthan, P., Kiruthika, C., Kumarasamy, P. & Balasubramanyam, D. (2014). Comparative study of linear mixed-effects and artificial neural network models for longitudinal unbalanced growth data of Madras Red sheep. Animal Sciense, 7(2): 58-52.

Ghavi Hosseinzadeh, N. & Ardalan, M. (2010). Estimation of genetic parameters for body weight traits and litter size of Moghani sheep. Journal of Agriculture Science Cambridge, 148: 363–370.

Ghorbani, Z., Sheikhlou, M.R., & Karimi, A. (2021). Estimation of genetic parameters of growth curve components obtained from comparison of nonlinear models in Moghani sheep breed. Journal of Ruminant Research, 9: 99- 113. (In Persian).

Ghotbaldini, H.R., Mohammadabadi, M.R., Nezamabadi Pour, H. (2018). Application of artificial intelligence for estimating breeding value of body weight in birth and 3 months' age in Kermani sheep breed. Modern Genetics, 3: 331- 323.

Izy, J. & Zarghi, H. (2015). Comparison of Regression and Artificial Neural Network Models in Predicting the Production Performance of Laying Hens. Iranian Journal of Animal Science Research, 7(1): 58-65. (In Persian)

Jafaroghli, M., Rashidi, A., Mokhtari, S.M., & Shadparvar, A.A. (2010). (Co)Variance components and genetic parameter estimates for growth traits in Moghani sheep. Small Ruminant Researh, 91:170–177.

Kaewtapee, C., Khatchaturant, V. & Bunchasak, C. (2011). Comparison of growth models between artificial neural networks and nonlinear regression analysis in Cheery vally ducks. Applied Poultry Research, 20: 421- 428.

Mirderikvandi, M., Masoudi, A., Azarfar, A. & Kiani, A. (2015). Comparison of Gompertz and artificial neutral network models of broiler growth received Artichoke extract in their drinking water.  Iranian Journal of Animal Science, 46(1): 9-16. (In Persian)

Muhammad, A., Eyduran, E., Tariq, M.M., Cem, T. & Ferhat, A. (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan Journal of Zoology, 47 (6): 1579- 1585.  

Safari, R., Sheikhlou, M.R., Esmaeilpour, M., Jafarzadeh, H. & Sheikhali Pour, A. (2024). Application of artificial neural networks to predict milk production in Holstein cows. Journal of Ruminant Research, 12: 113-128. (In Persian)

Torres, M., Hervas, C. & Amador, F., (2005). Approximating the sheep milk production curve through the use of artificial neural networks andgenetic algorithms. Computers and Operations Research, 32: 2653–70.

Zakizadeh, S., Saghi, D.A. & Memarian, H., (2020). Mathematical description of growth curve in Kurdish sheep using artificial neural network and its comparison with non-linear models. Animal Production Research, 9, 45-59. (In Persian)