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Prediction of feed abrasive value by artificial neural networks and multiple linear regression

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Abstract

In order to evaluate rapid testing methods based on the relationship between feed abrasive value (FAV) and physicochemical properties (particle size, bulk density, dry matter (DM), soluble dry matter, water-holding capacity (WHC), ash, crud protein, neutral detergent fiber (NDF), physically effective NDF and non-fibrous carbohydrates (NFC)), 100 empirical dataset were used. Relationships were investigated using multiple linear regression (MLR) and artificial neural networks (ANNs). The mean relative error was significantly (P < 0.01) lower for ANN than MLR model. Globally, the non-linear ANN model approach is shown to provide a better prediction of FAV than linear multiple regression.

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Authors and Affiliations

  1. Department of Animal Sciences, College of Aboureihan, University of Tehran, P.O. Box: 11365-4117, Tehran, Iran

    M. A. Norouzian

  2. Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

    S. Asadpour

Authors
  1. M. A. Norouzian
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  2. S. Asadpour
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Correspondence to M. A. Norouzian.

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Norouzian, M.A., Asadpour, S. Prediction of feed abrasive value by artificial neural networks and multiple linear regression. Neural Comput & Applic 21, 905–909 (2012). https://doi.org/10.1007/s00521-011-0579-5

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  • DOI: https://doi.org/10.1007/s00521-011-0579-5

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