[Adamczyk K., Molenda K., Szarek J., Skrzyński G. (2005). Prediction of bulls’slaughter value from growth data using artificial neural network. J. Centr. Europ. Agric., 6: 133-142.]Search in Google Scholar
[Adya M., Collopy F. (1998). How effective are neural networks at forecasting and prediction? A review and evaluation. J. Forecast., l; 481-495.10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.0.CO;2-Q]Search in Google Scholar
[Berg E.P., Engel B.A., Forrest J.C. (1998). Pork carcass composition derived fromaneural network model of electromagnetic scans. J. Anim. Sci., 76: 18-22.]Search in Google Scholar
[Eckert R., Żak G., Bereta A. (2014). Results of performance tested gilts (in Polish). Report on pig breeding in Poland. Kraków, IZ PIB XXXII: 35-48.]Search in Google Scholar
[Hervas C., Garrido A., Lucena B., Garcia N., De Pedro E. (1994). Near infrared spectroscopy for classification of Iberian pig carcasses using an artificial neural network. J. Near Infrared Spectrosc., 2: 177-184.]Search in Google Scholar
[Ichikawa H. (2003). Hierarchy neural networks as applied to pharmaceutical problems. Advanc. Drug Deliv. Rev., 55: 1119-1147.]Search in Google Scholar
[Klimas R., Klimiene A., Rimkevicius S. (2004). Efficiency of breeding pigs selection according to phenotypic evaluation of meatiness. Vet. Zoot., 27: 79-86.]Search in Google Scholar
[Kumar U.A. (2005). Comparison of neural networks and regression analysis: Anew insight. Expert Syst. with Applic., 29: 424-430.]Search in Google Scholar
[Lisiak D., Borzuta K. (2014). The influence of the SEUROPgrade and weight of pig carcasses on lean meat content evaluated using regression equations from 2003 and 2011 (in Polish). Rocz. Nauk. PTZ, 10: 65-75.]Search in Google Scholar
[Migdał - Najman K., Najman K. (2000). Neural networks, use for forecasting WIG (in Polish). Katedra Statystyki, Wydział Zarządzania, Uniwersytet Gdański, pp. 1-17. http://panda.bg.univ.gda.pl/prezes/WWW_A_moze_sieci_neuronowe.pdf]Search in Google Scholar
[Paliwal M., Kumar U.A. (2009). Astudy of academic performance of business school graduates using neural network and statistical techniques. Expert Syst. with Applic., 36: 7865-7872.]Search in Google Scholar
[Radović Č., Petrović M., Živković B., Radojković D., Parunović N., Brkić N., Delić N. (2013). Heritability, phenotypic and genetic correlations of the growth intensity and meat yield of pigs. Biotech. Anim. Husb., 29: 75-82.]Search in Google Scholar
[Różycki M., Tyra M. (2010). Methodology for evaluating the value of fattening and slaughter pigs carried out in the stations of performance control slaughter pigs (SKURTCh) (in Polish). Report on pig breeding in Poland. IZ PIB XXVIII: 93-117.]Search in Google Scholar
[Stricklin W.R.,de Bourcier P., Zhou J.Z., Gonyou H.W. (1998). Artificial pigs in space: using artificial intelligence and artificial life techniques to design animal housing. J. Anim. Sci., 76: 2609-2613.]Search in Google Scholar
[Subba Narasimha P.N., Arinze B ., Anandarajan M. (2000). The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: Exploration of some issues. Expert Syst. Applic., 19: 117-123.]Search in Google Scholar
[Szyndler-Nędza M., Eckert R. (2008). Relationships between live measurements of backfat and longissimus dorsi thickness and fatness as well as muscularity of carcass, ham and loin of boars and gilts (in Polish). Rocz. Nauk. PTZ., 4: 103-113.]Search in Google Scholar
[Ślipek Z., Francik S., Frączek J. (2003). Methodic aspects of creating ANNmodels in agrophysical research (in Polish). Acta Agrophysica, 2: 231-241.]Search in Google Scholar
[Xin H. (1999). Assessing swine thermal comfort by image analysis of postural behaviors. J. Anim. Sci., 77: 1-9.]Search in Google Scholar