Have a personal or library account? Click to login
Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks Cover

Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks

Open Access
|Jan 2016

References

  1. 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.
  2. 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
  3. 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.
  4. 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.
  5. 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.
  6. Ichikawa H. (2003). Hierarchy neural networks as applied to pharmaceutical problems. Advanc. Drug Deliv. Rev., 55: 1119-1147.
  7. Klimas R., Klimiene A., Rimkevicius S. (2004). Efficiency of breeding pigs selection according to phenotypic evaluation of meatiness. Vet. Zoot., 27: 79-86.
  8. Kumar U.A. (2005). Comparison of neural networks and regression analysis: Anew insight. Expert Syst. with Applic., 29: 424-430.
  9. 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.
  10. 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
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. Ślipek Z., Francik S., Frączek J. (2003). Methodic aspects of creating ANNmodels in agrophysical research (in Polish). Acta Agrophysica, 2: 231-241.
  18. Xin H. (1999). Assessing swine thermal comfort by image analysis of postural behaviors. J. Anim. Sci., 77: 1-9.
DOI: https://doi.org/10.1515/aoas-2015-0057 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402
Language: English
Page range: 275 - 286
Submitted on: Jul 23, 2015
|
Accepted on: Aug 31, 2015
|
Published on: Jan 23, 2016
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2016 Magdalena Szyndler-Nędza, Robert Eckert, Tadeusz Blicharski, Mirosław Tyra, Artur Prokowski, published by National Research Institute of Animal Production
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.