ABSTRACT An experiment was conducted to evaluate modelling relationships between pig’s body surface temperature and ambient environment including inside and outside of pig barn. For this purpose, four different artificial neural network (ANN), including Feed Forward Back-propagation (FFB), Layer recurrent (LR), Elman (EL) and Cascade Forward Back-propagation (CFB) with different learning algorithms, transfer functions, hidden layers and neuron in each layer, and multi-linear regression (MLR) models have been performed to predict body temperature of pig. Six two-month-old pigs were studied over a period of 92 days during two years (2017–2018) to develop and evaluate the ANN and MLR models. The performance of the models in predicting pig’s body temperature was determined using statistical quality parameters, including coefficient of determination (R 2), root mean square error (RMSE) and mean absolute percentage error (MAPE). The FFB model with the Levenberg-Marquardt training function, Gradient descent weight and bias learning function, Log-sigmoid transfer function and two hidden layers with 20 neurons was found as the best model. Sensitivity analysis indicated that the temperature-humidity index (THI) inside the room is the most influential factor in predicting pig’s body temperature in the MLR/ANN models.

中文翻译：

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人工神经网络和多元线性回归作为模拟猪体表温度的潜在方法

摘要 进行了一项实验来评估猪的体表温度与周围环境（包括猪舍内外）之间的建模关系。为此，四种不同的人工神经网络 (ANN)，包括前馈反向传播 (FFB)、层循环 (LR)、埃尔曼 (EL) 和级联正向反向传播 (CFB)，具有不同的学习算法、传递函数、隐藏层和每层神经元，并已执行多线性回归（MLR）模型来预测猪的体温。在两年（2017-2018 年）的 92 天内对六头两个月大的猪进行了研究，以开发和评估 ANN 和 MLR 模型。使用统计质量参数确定模型在预测猪体温方面的性能，包括决定系数 (R 2)、均方根误差 (RMSE) 和平均绝对百分比误差 (MAPE)。具有 Levenberg-Marquardt 训练函数、梯度下降权重和偏置学习函数、Log-sigmoid 传递函数和具有 20 个神经元的两个隐藏层的 FFB 模型被认为是最佳模型。敏感性分析表明，房间内的温湿度指数（THI）是MLR/ANN模型中预测猪体温最有影响的因素。