Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

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.

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

“…The experiment was carried out for twenty-one days, in which the birds were submitted to thermal challenges only in the second week of life-from the eighth day of life. During the first and third weeks of life, temperatures were maintained in the thermoneutrality zone, with air dry-bulb temperatures (tdb) of 33ºC and 27ºC, respectively (Cassuce et al, 2013;Ferraz et al, 2018). In the second week, a difference between treatments was established by the intensity and duration of thermal stress.…”

Decision Trees for Predicting the Physiological Responses of Rabbits

“…These parameters can be customized by the user to train the decision trees. Additionally, a further subset was used to test the overall system performance; this subset consisted of the mean values of the experimental measurements, according to the methodology proposed by [25]. In total, 21 mean values were used to test the decision tree performance (Table 1).…”

Fuzzy-genetic approaches to knowledge discovery and decision making: Estimation of the cloacal temperature of chicks exposed to different thermal conditions

“…The use of genetic algorithms has been shown to improve fuzzy inference models (Ferraz et al, 2018;Jha et al, 2018;Rodrêguez-Fdez et al, 2016). One of the biggest problems in executing this type of approach is in finding a methodology that helps with the implementation of the predicted models.…”

“…These models include linear or nonlinear regression models (RMs), models based on computational intelligence (e.g. artificial neural networks (ANNs) ((Hern andez-Julio, Yanagi, de F atima Avila Pires, Aur elio Lopes, & Ribeiro de Lima, 2014), neuro-fuzzy networks (NFNs) (Ferraz et al, 2014), fuzzy logic (FL) (Ferreira, Yanagi Junior, Lacerda, & Rabelo, 2012;Hern andez-Julio et al, 2015;Nascimento, Pereira, N€ aas, & Rodrigues, 2011;Pereira, Bighi, Gabriel Filho, & Gabriel, 2008), genetic algorithms (GA), and fuzzy genetic algorithms (Fuzzy-GA) (Ferraz et al, 2018;Jha, Ahmad, & Crowley, 2018).…”