利用神经网络预测牛肉关节的烘烤时间和重量损失

Guozhong Xie
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引用次数: 3

摘要

利用牛肉关节的烘烤时间和重量损失的实验数据对神经网络(NN)模型进行了训练和验证。建立了数学模型和响应面模型。神经网络模型和RS模型的预测结果几乎相同,并且优于数学模型。利用训练好的神经网络和RS模型,研究了空气温度、牛肉关节尺寸、牛肉关节重量和牛肉关节初始温度对烤时间和失重的影响。空气或牛肉初始温度的增加缩短了烘烤时间,但增加了重量损失。对于较大的牛肉关节,烘烤时间和重量损失都显著增加。用神经网络模型和RS模型计算烤肉时间和重量损失达到最大值的牛肉半径与长度的临界比为0.45,用神经网络模型计算烤肉时间和重量损失的临界比为0.55(神经网络模型)或0.6 (RS模型)。为了提高生产效率和减轻体重,建议使用小牛肉关节,避免使用临界比例的牛肉关节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use of neural networks to predict roasting time and weight loss for beef joints

A neural networks (NN) model was trained and validated using experimental data on roasting times and weight losses from beef joints. Mathematical and response surface (RS) models were also developed. Predicted results from NN and RS models were almost identical and better than the mathematical model. Using the trained NN and RS models, the effects of air temperature, dimension, weight of beef joint, its initial temperature on roasting time, and weight loss were investigated. An increase in air or initial beef temperature decreased roasting time but increased weight loss. For larger beef joints, both roasting time and weight loss increased significantly. Critical ratios of beef radius to length where roasting time and weight loss reached maximum values were found to be 0.45 using both NN and RS models for roasting time and 0.55 (NN model) or 0.6 (RS model) for weight loss. To improve productivity and reduce weight loss, small beef joints are recommended and beef joints with the critical ratios should be avoided.

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