Comparison of fuzzy and on/off controllers for winter season indoor climate management in a model poultry house

Mirzaee‐Ghaleh, Esmaeil; Omid, Mahmoud; Keyhani, Alireza; Dalvand, Mohammad Jafar

doi:10.1016/j.compag.2014.11.017

Cited by 44 publications

(29 citation statements)

References 28 publications

(39 reference statements)

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“…Thus, the fuzzy system can be used to predict the performance of broiler chickens under different intensities and durations of heat stress during the second week of life (WANG et al, 2008;ABORISADE & STEPHEN, 2014;MIRZAEE-GHALEH et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy modeling of broiler performance, raised from 1 to 21 days, subject to heat stress

Abreu¹,

Yanagi²,

Fassani³

et al. 2015

Eng. Agríc.

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Given the need to obtain systems to better control broiler production environment, we performed an experiment with broilers from 1 to 21 days, which were submitted to different intensities and air temperature durations in conditioned wind tunnels and the results were used for validation of a fuzzy model. The model was developed using as input variables: duration of heat stress (days), dry bulb air temperature (°C) and as output variable: feed intake (g) weight gain (g) and feed conversion (g.g -1 ). The inference method used was Mamdani, 20 rules have been prepared and the defuzzification technique used was the Center of Gravity. A satisfactory efficiency in determining productive responses is evidenced in the results obtained in the model simulation, when compared with the experimental data, where R 2 values calculated for feed intake, weight gain and feed conversion were 0.998, 0.981 and 0.980, respectively. KEYWORDS: fuzzy logic, animal environment, poultry. MODELAGEM FUZZY DO DESEMPENHO DE FRANGOS DE CORTE, CRIADOS DE 1 A 21 DIAS, SUBMETIDOS A ESTRESSE TÉRMICORESUMO: Diante da necessidade de se obter sistemas, para melhor controlar o ambiente de produção de frangos de corte, foi realizado um experimento com frangos de corte de 1 a 21 dias, os quais foram submetidos a diferentes intensidades e durações de temperaturas do ar em túneis de vento climatizados, e os resultados foram utilizados para a validação de um modelo fuzzy. O modelo foi desenvolvido, utilizando como variáveis de entrada: duração do estresse térmico (dias), temperatura de bulbo seco do ar (°C) e, como variável de saída: consumo de ração (g), ganho de peso (g) e conversão alimentar (g.g -1 ). O método de inferência utilizado foi o de Mamdani, tendo sido elaboradas 20 regras, e a técnica de defuzzificação usada foi a do Centro de Gravidade. Nos resultados, obtidos na simulação do modelo, quando comparados com os dados experimentais, evidencia-se uma eficiência satisfatória na determinação das respostas produtivas, nas quais os valores de R 2 calculados, para consumo de ração, ganho de peso e conversão alimentar, foram de 0,998; 0,981 e 0,980, respectivamente. PALAVRAS-CHAVE:Lógica fuzzy, ambiência animal, avicultura.

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Section: Introductionmentioning

confidence: 99%

Fuzzy modeling of broiler performance, raised from 1 to 21 days, subject to heat stress

Abreu¹,

Yanagi²,

Fassani³

et al. 2015

Eng. Agríc.

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“…However, analyzing the intensity and duration together makes it possible to investigate possible occurrences of adaptation of the bird to the stressful environment, depending on the exposure time, or to verify how stressor intensity can aggravate productivity losses due to longer or shorter exposure times. Thus, the control of environmental variables becomes essential for the process of rearing broilers (Cassuce et al, 2013;Mirzaee-Ghaleh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Due to a number of factors involving the thermal environment of a broiler cutting installation and its interaction with the physiological and productive responses of birds, artificial intelligence has been shown to be an interesting methodology to assist in the decision-making process. For this reason, the main aim of this work was to develop an artificial neural network (ANN) to predict feed conversion (FC), water consumption (Cwater), and cloacal temperature (tclo) of broilers submitted to different air dry-bulb temperatures (24, 27, 30, and 33ºC) and durations (1, 2, 3, and 4 days) of thermal stress in the second week of the production cycle. Relative humidity and wind speed were fixed at 60% and 0.2 ms -1 , respectively. The experimental data were used for the development of an ANN with supervised training using the Levenberg-Marquardt backpropagation algorithm. The ANN consisted of three input layers one hidden, and three output with sigmoidal tangent transfer functions with values between -1 and 1. The developed ANN has adequate predictive capacity, with coefficients of determination (R 2 ) for tclo, FC, and Cwater of 0.79, 0.87, and 0.97, respectively. In this way, the proposed ANN can be used as a support for decision-making to trigger poultry heating systems for broiler breeding.

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“…The microclimatic conditions in the breeding environment have a strong effect on the thermal comfort of broiler chickens (Mirzaee-Ghaleh et al, 2015). Among the environmental factors, air temperature and relative humidity are the most relevant to poultry because they affect the control of body temperature and heat exchange for maintaining homeothermy (Costa et al, 2012;Ferreira et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Performance of Fuzzy Inference Systems to Predict the Surface Temperature of Broiler Chickens

et al. 2018

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This study aimed to compare fuzzy systems with different configurations to predict the surface temperature (ts) of broiler chickens subjected to different intensities and durations of thermal challenges in the second week of life. Data on the ts of broiler chickens aged 8 to 11 days were acquired by infrared thermography and subjected to combinations of four dry-bulb temperatures (tdb) (24, 27, 30, and 33 ºC) and four durations of thermal challenges (DTC) (1, 2, 3, or 4 days). The input variables of the fuzzy systems were tdb and DTC, and the output variable was ts. The Mamdani inference method involving five defuzzification methods [center of gravity (centroid), bisector of the area (bisector), largest of maximum (lom), middle of maximum (mom), and smallest of maximum (som)], and Sugeno inference with two defuzzification methods [weighted average (wtaver) and weighted sum (wtsum)] were evaluated. For both inference methods, triangular and Gaussian pertinence functions were tested for input and output variables, except for Sugeno inference, which used singletons functions as output variables. While developing fuzzy systems, different configurations must be compared, and the system with smaller simulation errors should be selected.

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Comparison of fuzzy and on/off controllers for winter season indoor climate management in a model poultry house

Cited by 44 publications

References 28 publications

Fuzzy modeling of broiler performance, raised from 1 to 21 days, subject to heat stress

Fuzzy modeling of broiler performance, raised from 1 to 21 days, subject to heat stress

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

Performance of Fuzzy Inference Systems to Predict the Surface Temperature of Broiler Chickens

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