Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures—back-propagation-3, Ward-5, and the general regression neural network—were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R² of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production.

开发了几种数学或统计和人工智能模型来比较商业蛋鸡的产蛋量预测。这些模型的初始数据是从奥本大学家禽研究农场对商业菌株进行的比较层试验中收集的。通过使用 22 种商业菌株产蛋量的均值和 SD，产生了模拟数据以代表新情况。从模拟数据中，生成随机示例用于神经网络训练和测试，用于从第 22 周到第 36 周的每周产蛋量预测。三种神经网络架构——反向传播 3、Ward-5 和一般回归神经网络比较它们预测鸡蛋产量的效率，以及其他传统模型。一般回归神经网络给出了最佳拟合线，²的 0.71。将一般回归神经网络预测曲线与原始产蛋数据、白壳和棕壳菌株的平均曲线、线性回归预测和 Gompertz 非线性模型进行比较。通用回归神经网络在所有这些比较中都更胜一筹，如果初始过度预测得到有效管理，则它可能是首选模型。一般而言，神经网络模型高效、易于使用、需要的数据较少，并且在农场管理条件下可用于预测产蛋量。