Performance of expert fuzzy systems for prediction of rabbit feed intake after weaning

The postweaning phase is stressful for rabbits due to maternal separation and the introduction of solid feed. Stress can be aggravated when animals are subjected to thermal discomfort. Therefore, thermal variables, such as air temperature, which influence the productive and physiological performance of animals, require greater control in this phase of life of rabbits to ensure their well-being and productive efficiency. Thus, the objective of the present study was to develop and compare fuzzy inference systems (FISs) with different configurations to predict the feed intake (FI) of New Zealand White (NZW) rabbits subjected to different thermal conditions after weaning. The experiment lasted 14 days, and twelve rabbits between 30 and 43 days old were used. The animals were housed in air-conditioned wind tunnels and subjected to air temperatures of 20, 24, 28 and 32 °C. For the FIS configurations, Mamdani inference with five defuzzification methods (center of gravity (COG), bisector of area (BOA), largest of maximum (LOM), mean of maximum (MOM) and smallest of maximum (SOM)) and Sugeno inference with two defuzzification methods (weighted average (WA) and weighted sum (WS)), were evaluated. In both inference methods, the input variables (air temperature and time after weaning) were represented by triangular, Gaussian or trapezoidal functions. In turn, the output variable (FI, g) was represented by triangular, Gaussian or trapezoidal functions in the Mamdani FIS and by singleton functions in the Sugeno FIS. Thus, all developed FISs were validated, and their results were compared to the experimental data using statistical indices. As a result, adequate FI prediction performances were obtained for rabbits using both inference methods, regardless of the configurations used in their development. However, the smallest simulation errors were obtained using the Sugeno FIS with Gaussian inputs and WA defuzzification and is therefore a system with greater generalization capacity for unknown scenarios. Thus, the developed models can be used as a support system for decisions on the management of rabbits, aiding the efficient production and welfare of the animals, as well as the maintenance of thermal variables through the activation of installed climate systems inside the rabbit production environment.Trial registration number: 085/17. Date of registration: 14/12/2017.