Pertinence Curves in Fuzzy Modeling of the Productive Responses of Broilers

Abstract

The selection of the type of fuzzy systems pertinence curve allows a better representation of the mathematical model and a smaller simulation error. We aimed to study the effect of pertinence curves in fuzzy modeling of broiler performance, created in different production systems. For the development and testing of fuzzy models, three commercial aviaries (conventional, tunnel with negative pressure, and dark house) were evaluated over one year, totaling six lots per system. For the development of the model, the input variables were enthalpy in each rearing phase (initial: phases 1, 2, and 3; growth: phase 4; and final: phase 5) and the output variables were feed intake (FE), weight gain (GP), feed conversion (FE), and the productive efficiency index (PEI). Triangular, trapezoidal, and Gaussian pertinence curves were combined and applied to represent the input and output fuzzy sets, totaling nine fuzzy models for each output variable. The combinations of pertinence curves provided adequate responses for the prediction of AL, GP, RC, and PEI. However, the selection of the types of curves should be studied on a case-by-case basis, so that the smallest possible simulation errors are obtained.

Fuzzy Modeling for Rapid Cooling of Table Grapes in Different Plastic Film Bags

“…The input variables used for the development of the system were the mean RH (RHm), RH amplitude (λRH), and plastic film bags, represented by trapezoidal pertinence curves (Figure 1). In a study by Lourençoni et al (2019a), when evaluating different combinations of pertinence curves, all combinations provided adequate prediction responses. Based on the input variables and using the experimental data as a reference, the fuzzy model predicted weight loss (WL) as the output variable, which was characterized by a trapezoidal pertinence curve (Figure 2).…”