Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 127130, 9 pages
Research Article

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

1Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2Center of Excellence: Control and Intelligent Processing, School of Electrical and Computer Engineering, University of Tehran, Iran
3Department of Dairy Science, University of Wisconsin-Madison, Madison, WI 53706, USA

Received 12 May 2012; Revised 9 August 2012; Accepted 9 August 2012

Academic Editor: Chunmei Liu

Copyright © 2012 Saleh Shahinfar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.