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Neural network committee to predict the AMEn of poultry feedstuffs

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Abstract

A committee of neural networks is the aggregation of two or more neural networks for making overall predictions that are supposedly more accurate than those obtained by the individual networks. The objective of this paper was to assign some uncertainty over the predictions of neural networks, using a network committee to estimate the nitrogen-corrected metabolizable energy (AMEn) values of the energetic and protein concentrate feedstuffs for broilers. The dataset used to implement each expert network contains 568 experimental results. Another dataset with 48 bioassay results was used as test data. From several implemented multilayer perceptrons, the networks that presented the best generalization performance were selected to constitute the committee. The percentage of correct predictions was used as the criterion to compare committees that contained different numbers of networks. The highest probability density intervals were obtained for each feedstuff in the test data in this comparison. The estimator that ensured more accurate predictions was selected. The highest accuracy for predicting the AMEn values of concentrate feedstuffs for broilers was achieved by a committee with 1,000 networks with the use of the mode of the empirical distribution obtained from 1,000 estimated values of the AMEn. The accuracy of the models was evaluated based on their values of error measures between the observed and predicted values, in which the mode of the empirical distribution presented lower values of mean squared error (MSE = 45,285.43), mean absolute deviation (MAD = 177.66) and mean absolute percentage error (MAPE = 5.97 %) compared to the mean and the median.

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References

  1. Ahmadi H, Mottaghitalab M, Nariman-Zadeh N (2007) Group method of data handling-type neural network prediction of broiler performance based on dietary metabolizable energy, methionine, and lysine. J Appl Poult Res 16:494–501. doi:10.3382/japr.2006-00074

    Article  Google Scholar 

  2. Ahmadi H, Golian A, Mottaghitalab M, Nariman-Zadeh N (2008) Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling-type neural network. Poult Sci 87:1909–1912. doi:10.3382/ps.2007-00507

    Article  Google Scholar 

  3. Alvarenga RR, Rodrigues PB, Zangeronimo MG, Freitas RTF, Lima RR, Bertechini AG, Fassani EJ (2011) Energetic values of feedstuffs for broilers determined with in vivo assays and prediction equations. Anim Feed Sci Technol 168:257–266. doi:10.1016/j.anifeedsci.2011.04.092

    Article  Google Scholar 

  4. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, UK

    Google Scholar 

  5. Bucene LC, Rodrigues LHA (2004) Utilização de redes neurais artificiais para avaliação de produtividade do solo, visando classificação de terras para irrigação. Revista Brasileira de Engenharia Agrícola e Ambiental 8:326–329. doi:10.1590/S1415-43662004000200025

    Article  Google Scholar 

  6. Can M (2013) Committee Machine Networks to Diagnose Cardiovascular Diseases. Southeast Europe J Soft Comput 2:76–83

    Google Scholar 

  7. Chryssolouris G, Lee M, Ramsey A (1996) Confidence interval prediction for neural network models. IEEE Trans Neural Netw 7:229–232. doi:10.1109/72.478409

    Article  Google Scholar 

  8. Cybenko G (1988) Continuos valued neural network with two hidden layers are sufficient. Technical Report, Department of Computer Science, Tufts University, Medford, MA, USA

    Google Scholar 

  9. da Silva IN, Spatti DH, Flauzino RA (2010) Rede Neurais Artificiais: para Engenharia e Ciências Aplicadas. Ed. Artliber, São Paulo

    Google Scholar 

  10. Goldschmidt RR (2010) Uma Introdução à Inteligência Computacional: fundamentos, ferramentas e aplicações. 1ed. Rio de Janeiro: IST-Rio

  11. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993. doi:10.1109/72.329697

    Article  Google Scholar 

  12. Haider A, Hanif MN (2009) Inflation forecasting in Pakistan using artificial neural networks. Pak Econ Soc Rev 47:123–138

    Article  Google Scholar 

  13. Haykin S (2007) Neural networks—a comprehensive foundation, 3rd edn. Prentice-Hall Inc., Upper Saddle River, NJ, USA

    Google Scholar 

  14. Hunter D, Wilamowski B (2011) Parallel multi-layer neural network architecture with improved efficiency. International conference on human system interaction (HSI 2011), Yokohama, Japan

  15. Hwang JTG, Ding AA (1997) Prediction intervals for artificial neural networks. J Am Stat Assoc 92:748–757. doi:10.1080/01621459.1997.10474027

    Article  MATH  MathSciNet  Google Scholar 

  16. Kenari SAJ, Mashohor S (2013) Robust committee machine for water saturation prediction. J Petrol Sci Eng 104:1–10. doi:10.1016/j.petrol.2013.03.009

    Article  Google Scholar 

  17. Lima CAM (2004) Comitê de Máquinas: Uma Abordagem Unificada Empregando Máquinas de Vetores-Suporte. Doctoral thesis, Universidade Estadual de Campinas

  18. Lipnickas A (2008) Adaptive Committees of Neural Classifiers. Inf Technol Control 37:205–210

    Google Scholar 

  19. Lovatto PA, Lehnen CR, Andretta I, Carvalho AD Hauschild L (2007) Meta-analysis in scientific research: a methodological approach. Braz J Animal Sci 36(Suppl.):285–294. doi:10.1590/S1516-35982007001000026

  20. MATLAB 7.12 R2011a (2011) The Math Works, Inc. http://www.mathworks.com/products/neuralnet/. Access on: 25 ago. 2013

  21. Mariano FCMQ, Paixão CA, Lima RR, Alvarenga RR, Rodrigues PB, Nascimento GAJ (2013) Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks. Animal 7:1440–1445. doi:10.1017/S1751731113000712

    Article  Google Scholar 

  22. Nascimento GAJ, Rodrigues PB, Freitas RTF, Bertechini AG, Lima RR, Pucci LEA (2009) Prediction equations to estimate the energy values of plant origin concentrate feeds for poultry utilizing the meta-analysis. Braz J Anim Sci 38:1265–1271. doi:10.1590/S1516-35982009000700015

    Google Scholar 

  23. Nilsson NJ (1965) Learning machines: foundations of trainable pattern-classifying systems. Macgraw-Hill, New York

    MATH  Google Scholar 

  24. Oliveira RC, Acevedo NIA, Silva Neto AJ, Biondi Neto L (2010) Aplicação de um comitê de redes neurais artificiais para a solução de problemas inversos em Transferência Radiativa. TEMA Tend Mat Apl Comput 11:171–182. doi:10.5540/tema.2010.011.02.0171

    MathSciNet  Google Scholar 

  25. Perai AH, Moghaddam HN, Asadpour S, Bahrampour J, Mansoori GH (2010) A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal. Poult Sci 89:1562–1568. doi:10.3382/ps.2010-00639

    Article  Google Scholar 

  26. R DEVELOPMENT CORE TEAM (2013) R: a language and environment for statistical computing. Vienna: R foundation for statistical computing. http://www.r-project.org. Access on: 10 October 2013

  27. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. Proceedings of the IEEE international conference on neural networks (ICNN), pp 586–591, San Francisco

  28. Rustempasic I, Can M (2013) Diagnosis of Parkinson’s disease using principal component analysis and boosting committee machines. Southeast Europe J Soft Comput 2:102–109

    Google Scholar 

  29. Sauvant D, Schmidely P, Daudin JJ, St-Pierre NR (2008) Meta-analyses of experimental data in animal nutrition. Animal 2:1203–1214. doi:10.1017/S1751731108002280

    Article  Google Scholar 

  30. Shao R, Martin EB, Zhang J, Morris AJ (1997) Confidence bounds for neural network representations. Comput Chem Eng 21(Suppl.):S1173–S1178. doi:10.1016/S0098-1354(97)87661-2

  31. Sibbald IR, Slinger SJ (1963) A biological assay for metabolizable energy in poultry feed ingredients together with findings which demonstrate some of the problems associated with evaluation of fats. Poult Sci 42:13–25. doi:10.3382/ps.0420313

    Google Scholar 

  32. Siwek K, Osowski S, Szupiluk R (2009) Ensemble neural network approach for accurate load forecasting in a power system. Int J Appl Math Comput Sci 19:303–315. doi:10.2478/v10006-009-0026-2

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge financial support provided by the Coordination Improvement of Higher Education Students (CAPES).

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Authors and Affiliations

  1. Department of Exact Science, Federal University of Lavras, P.O. Box 3037, Lavras, Minas Gerais, 37200-000, Brazil

    F. C. M. Q. Mariano & R. R. Lima

  2. Department of Animal Science, Federal University of Lavras, P.O. Box 3037, Lavras, Minas Gerais, 37200-000, Brazil

    R. R. Alvarenga & P. B. Rodrigues

  3. Department of Computer Science, Federal University of Lavras, P.O. Box 3037, Lavras, Minas Gerais, 37200-000, Brazil

    W. S. Lacerda

Authors
  1. F. C. M. Q. Mariano
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  2. R. R. Lima
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  3. R. R. Alvarenga
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  4. P. B. Rodrigues
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  5. W. S. Lacerda
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Correspondence to F. C. M. Q. Mariano.

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Mariano, F.C.M.Q., Lima, R.R., Alvarenga, R.R. et al. Neural network committee to predict the AMEn of poultry feedstuffs. Neural Comput & Applic 25, 1903–1911 (2014). https://doi.org/10.1007/s00521-014-1680-3

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  • DOI: https://doi.org/10.1007/s00521-014-1680-3

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