Comparison of Artificial Neural Network and Multiple Regression Analysis for Prediction of Fat Tail Weight of Sheep
الموضوعات :م.ع. نوروزیان 1 , م. وکیلی علویجه 2
1 - Department of Animal Science, College of Abouraihan, University of Tehran, Tehran, Iran
2 - Department of Mathematics, Faculty of Mathematical Science, Shahid Beheshti University, Tehran, Iran
الکلمات المفتاحية: Artificial Neural Network, Sheep, multiple linear regression, fat tail,
ملخص المقالة :
A comparative study of artificial neural network (ANN) and multiple regression is made to predict the fat tail weight of Balouchi sheep from birth, weaning and finishing weights. A multilayer feed forward network with back propagation of error learning mechanism was used to predict the sheep body weight. The data (69 records) were randomly divided into two subsets. The first subset is the training set comprising of 75 percent data (52 records) to build the neural network model and test data set comprising of 25 percent (17 records), which is not used during the training and is used to evaluate performance of different models. The mean relative error was significantly (P<0.01) lower for ANN than the MLR model. The coefficient of determination (R2) values computed for the body measurements were generally higher (0.93) using ANN model than themultiple linear regression (MLR) model (0.81). The ANN model improved the mean squared error (MSE) of the MLR model by 59% and R2 by 15% that the ANN represents a valuable tool for predicting of lamb fat tail weight from birth, weaning and finishing weights.
Alp M. and Cigizoglu H.K. (2007). Suspended sediment load simulation by two artificial neural network methods using hydromete- orological data. Environ. Model Softw. 22, 2-13.
Berry M.J.A. and Linoff G. (1997). Data Mining Techniques. John Wiley and Sons, New York.
Cybenko G.C. (1989). Approximations by super positions of a sigmoidal function. Math. Cont. Sig. Sys. 2(3), 303-314.
Davidson A. (2006). The Oxford Companion to Food. OxfordUniversityPress, USA.
Dayhoff J.E. (1990). Neural network architectures. Van Nostrand Reinhold, New York.
Fernandez C., Soria S., Sanchez-Seiquer E.P., Gomez-Chova S., Magdalen E.R., Martin-Guerrero J.D., Navarro M.D. and Serrano A.J. (2007). Weekly milk prediction on dairy goats using neural networks. Neural Comput. Appl. 16, 373-381.
Kashan N.E.J., Manafi Azar G.H., Afzalzadeh A. and Salehi A. (2005). Growth performance and carcass quality of fattening lambs from fat-tailed and tailed sheep breeds. Small Rumin. Res. 60, 267-271.
Marengo E., Bobba M., Robotti E. and Liparota M.C. (2006). Modeling of the polluting emissions from a cement production plant by partial least squares, principal component regression, and artificial neural networks. Environ. Sci. Technol. 40, 272-280.
Moradi M.H., Nejati-Javaremi A., Moradi-Shahrbabak M., Dodds K.G. and McEwan J.C. (2012). Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 13, 10-12.
Norouzian M.A. and Asadpour S. (2012). Prediction of feed abrasive value by artificial neural networks and multiple linear regression. Neural Comput. Appl. 21, 905-909.
Safdarian M.M., Zamiri M.I., Hashemi M. and Noorolahi H. (2008). Relationships of fat-tail dimensions with fat-tail weight and carcass characteristics at different slaughter weights of Torki-Ghashghaii sheep. Meat Sci. 80, 686-689.
Vatankhah M. and Talebi M.A. (2008). Heritability estimates and correlations between production and reproductive traits in Lori-Bakhtiari sheep in Iran. South African J. Anim. Sci. 38(2), 110-118.
Zamiri M.J. and Izadifard J. (1997). Relationships of fat-tail weight with fat-tail measurements and carcass characteristics of Mehraban and Ghezel rams. Small Rumin. Res. 15, 261-266.
