Abstract
ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.
Highlights
For the poultry industry to reach levels of excellence it has sought to improve productivity, without, increasing production costs (Ponciano et al, 2011)
The respiratory rate (RR) is the first physiological response that the birds use in order to maintain and control homeothermy, releasing internal heat by evaporation to the environment, which is the most efficient mechanism when animals are subjected to temperatures above the comfort temperatures (Saraiva et al, 2011)
According to Georgieva (2016), the main objective of Genetic Fuzzy Rule-Based System (GFRBS) is to adjust the parameters of input and output variables of the fuzzy model for the purpose of increasing or not, the accuracy of prediction of the output variable values (RR) and reduce the prediction error
Summary
For the poultry industry to reach levels of excellence it has sought to improve productivity, without, increasing production costs (Ponciano et al, 2011). The respiratory rate (RR) is the first physiological response that the birds use in order to maintain and control homeothermy, releasing internal heat by evaporation to the environment, which is the most efficient mechanism when animals are subjected to temperatures above the comfort temperatures (Saraiva et al, 2011). When considering the various possibilities of thermal environment associations and the estimation of animal welfare, the application of the fuzzy inference system can be presented as a favorable methodology (Nascimento et al, 2016). The genetic algorithms techniques arose in order to improve and refine the modeling of Fuzzy Systems (FIS). Their use stands out because of the possibility of sorting data for a purpose (Fogel et al, 2004)
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