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Application of Neural Networks in Animal Science

Application of Neural Networks in Animal Science

Emilio Soria, Carlos Fernández, Antonio J. Serrano, Marcelino Martínez, José R. Magdalena, Juan F. Guerrero
Copyright: © 2010 |Pages: 17
ISBN13: 9781605667669|ISBN10: 1605667668|EISBN13: 9781605667676
DOI: 10.4018/978-1-60566-766-9.ch021
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MLA

Soria, Emilio, et al. "Application of Neural Networks in Animal Science." Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., IGI Global, 2010, pp. 440-456. https://doi.org/10.4018/978-1-60566-766-9.ch021

APA

Soria, E., Fernández, C., Serrano, A. J., Martínez, M., Magdalena, J. R., & Guerrero, J. F. (2010). Application of Neural Networks in Animal Science. In E. Olivas, J. Guerrero, M. Martinez-Sober, J. Magdalena-Benedito, & A. Serrano López (Eds.), Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (pp. 440-456). IGI Global. https://doi.org/10.4018/978-1-60566-766-9.ch021

Chicago

Soria, Emilio, et al. "Application of Neural Networks in Animal Science." In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., 440-456. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-766-9.ch021

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Abstract

Stock breeding has been one of the most important sources of food and labour throughout human history. Every advance in this field has always led to important and beneficial impacts on human society. These innovations have mainly taken place in machines or genetics, but data analysis has been somewhat ignored. Most of the published works in data analysis use linear models, and there are few works in the literature that use non-linear methods for data processing in stock breeding where these methods have proven to obtain better results and performance than linear, classical methods. This chapter demonstrates the use of non-linear methods by presenting two practical applications: milk yield production in goats, and analysis of farming production systems.

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