Abu Qdais H, Bani Hani K and Shatnawi N, 2010. Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources. Conservation and Recycling 54: 359–363.
Ahmed S and Simonovic SP, 2005. An artificial neural network model for generating hydrograph from hydro meteorological parameters. Journal of Hydrology 315: 236-251.
Akbas Y, Taskin T and Demiroren E, 1999. Comparison of several models to fit the growth curves of Kivircik and Daglic male lambs. Turk J Vet Animal Science 23:537–554.
Aman Ullah M, Amin M and Ansar Abbas M, 2013. Non-Linear Regression Models to Predict the Lamb and Sheep Weight Growth. Pakistan Journal of Nutrition 12 (9): 865-869, ISSN 1680-5194.
Bahreini Behzadi MR and Aslaminejad AA, 2010. A Comparison of Neural Network and Nonlinear Regression Predictions of Sheep Growth. Journal of Animal and Veterinary Advances 16: 2128-2131.
Bahreini Behzadi MR, 2014. Comparison of different growth models and Artificial Neural Networks for the growth curve of Lori-Bakhtiari sheep. Journal of Rresearch in Ruminants Volume 3, Issue 2, 14: 57- 68.
Bahreini Behzadi MR, Aslaminejad AA, Sharifi AR and Simianer H, 2014. Comparison of Mathematical Models for Describing the Growth of Baluchi Sheep. Journal of Agric Sci Tech 14: 57-68.
Ben Hamouda M and Atti N, 2011. Comparison of growth curves of lamb fat tail measurements and their relationship with body weight in Babarine sheep. Small Ruminant Research. Volume 95, Issues 2–3, Pages 120–127.
Cartwright HM, 2008. Artificial neural networks in biology and chemistry. In: Artificial neuralnetworks: methods and applications. Livingstone, D. (Ed.), 1-13, Humana Press. ISBN: 978-1-58829-718-1, New York.
Da Silva LSA, Fraga AB, De Lima Da Sliva F, Beelen PMG, De Oliveira Silva RM, Tonhati H and Da Costa Baroos C, 2012. Growth curve in Santa Ines sheep. Small Ruminant Research 105: 182-185.
Daskiran I, Koncagul S and Bingol M, 2010. Growth Characteristics of Indigenous Norduz Female and Male Lambs. Journal of Agricultural Sciences 16: 62-69.
Falconer DS, 1989. Introduction to quantitative genetics. 3rd editation. Longman, Essex.
Ferentinos KP, 2005. Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms. Elsevier, Neural Networks 18: 934–950.
Gbangboche AB, Glele-Kakai R, Salifou S, Albuquerque LG and Leroy PL, 2008. Comparison of non-linear growth models to describe the growth curve in West African Dwarf sheep. Animal. 2:7, pp: 1003–1012.
Goliomytis M, Orfanos S, Panopoulou E and Rogdakis E, 2006. Growth curves for body weight and carcass components, and carcass composition of the Karagouniko sheep, from birth to 720 d of age. Small Ruminant Research. 66:222–229.
Grzesiak W, Błaszczyk P and Lacroix R, 2006. Methods of predicting milk yield in dairy cows—Predictive capabilities of Wood’s lactation curve and artificial neural networks (ANNs). Computers and Electronics in Agriculture. 54:69–83.
Hentzen W, 1995. Programming visual FoxPro 3.0. Ziff-Davis press. ISBN-10: 1562763253.
Jaffrezic F and Pletcher SD, 2000. Statistical Models for Estimating the Genetic Basis of Repeated Measures and Other Function-Valued Traits. Genetics 156(2): 913-22.
Kominakis AP, Abas Z, Maltaris I and Rogdakis E, 2002. A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Computers and Electronics in Agriculture 35: 35–48.
Koncagul S and Cadirci S, 2010. Analysis of Growth Curve of Broiler with Restricting and Unrestricting Initial Body Weight in Gompertz-Laird Model in Different Environments. Italian Journal of Animal Science 9: 20–25.
Kopuzlu S, Sezgin E, Esenbuga N and Cevdet Bilgin O, 2013. Estimation of growth curve characteristics of Hemsin male and female sheep. Journal of Applied Animal Research. Volume 42, 228-232.
Lacroix R, Salehi F, Yang XZ and Wade KM, 1997. Effects of data preprocessing on the performance of artificial neural networks for dairy yield prediction and cow culling classification. Transactions of the ASAE (American Society of Agricultural Engineers) 40(3): 839-846.
Lupi TM, Nogales S, Leon JM, Barba C and Delgado JV, 2015. Characterization of commercial and biological growth curves in the Segurena sheep breed. Animal. 9(8):1341-8.
Malhado CHM, Carneiro PLS, Affonso PRAM, Souza AAO and Sarmento JLR, 2009. Growth curves in Dorper sheep crossed with the local Brazilian breeds, Morada Nova, Rabo Largo, and Santa Inês. Small Ruminant Research. 84:16–21.
McManus C, Evangelista C, Fernandes LAC, de Miranda RM, Moreno-Bernal FE and dos Santos NR, 2003. Parameters for Three Growth Curves and Parameters that Influence Them for Bergamasca Sheep in the Brasilia Region. Revista Brasileira de Zootecnia (Brazilian Journal of Animal Science). 32: 1207–1212.
Olegario de Araujo R, Righetti Marcondes C, Cecilia Florisbal Dame M, del Valle Garnero A, Jose Gunski R, Magda Everling D and Nogara Rorato PR, 2012. Classical nonlinear models to describe the growth curve for Murrah buffalo breed. Ciência Rural, Santa Maria.42:520-525.
Roush WB, Dozier WA and Branton SL, 2006. Comparison of Gompertz and Neural Network Models of Broiler Growth. Poultry Science 85:794–797.
Sargolzaei M, Iwaisaki H and Colleau JJ, 2006. Contribution, Inbreeding F, Coancestry (CFC). A software package for pedigree analysis and monitoring genetic diversity. Release 1.0. Niigata University. Niigata 950-2181, Japan.
SAS Institute Inc, 2004. SAS/STAT® User’s Guide, Version 9.1. SAS Institute Inc., Cary, NC. ISBN 1-59047-243-8.
Shahinfar S, Mehrabani-Yeganeh H, Lucas C, Kalhor A, Kazemian M. and Weigel KA, 2012. Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems. Computational and Mathematical Methods in Medicine. Volume 2012 (2012), Article ID 127130, 9 pages.
Svozil D, KvasniEka V and Pospichal J, 1997. Tutorial Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems. 39: 43-62.
Tariq MM, Iqbal F, Eyduran E, Bajwa MA, Huma ZE and Waheed A, 2013. Comparison of Non-Linear Functions to Describe the Growth in Mengali Sheep Breed of Balochistan. Pakistan J. Zool., vol. 45(3), pp. 661-665.
Tekel N, Sireli HD, Elicin M and Elicin A, 2005. Comparison of growth curve models on Awassi lambs. Indian Vet. J., 82:179 – 182.
Topal M, Ozdemir M, Aksakal V, Yildiz N and Dogru U, 2004. Determination of the best nonlinear function in order to estimate growth in Morkaraman and Awassi lambs. Small Ruminant Research. 55:229–232.
Torres M, Hervas C and Amador F, 2005. Approximating the sheep milk production curve through the use of artificial neural networks andgenetic algorithms. Computers & Operations Research 32:2653–2670.
Vitezica ZG, Marie-Etancelin C, Bernadet MD, Fernandez X and Robert-Granie C, 2010. Comparison of nonlinear and spline regression models for describing mule duck growth curves. Poultry Science 89(8):1778-84.
Yazdi MH, Engstrom G, Nasholm A, Johansson K, Jorjani H and Liljedahl LE, 1997. Genetic parameters for lamb weight at different ages and wool production in Baluchi sheep. Anim Sci 65:247–255.
)