Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Abstract

The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.