使用多重回归模型和基于康奈尔净碳水化合物和蛋白质系统的逆向传播神经网络在体外甲生产的比较预测
Guanghui Yu1, Zenghui Li1, Ruilan Dong1
1College of Animal Science and Technology, Qingdao Agricultural University, No. 700 Changcheng Road, Chengyang District, Qingdao 266109, China.
Veterinary sciences
|November 26, 2025
概括
预测肉牛的甲 (CH4) 生产对于减少全球变暖和能源损失至关重要. 这项研究发现,反向传播神经网络 (BPNN) 模型比多重线性回归 (MLR) 模型更准确地预测料碳水化合物的CH4排放.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 环境科学 环境科学
背景情况:
- 来自乳头发酵的甲 (CH4) 生产有助于全球变暖,并代表肉牛的能量损失.
- 准确预测CH4排放对于减轻环境影响和提高料效率至关重要.
研究的目的:
- 为了比较多重线性回归 (MLR) 和反向传播神经网络 (BPNN) 模型的预测准确性,用于食CH4生产.
- 在康奈尔净碳水化合物和蛋白质系统 (CNCPS) 框架内根据碳水化合物 (碳水化合物) 组件评估这些模型.
- 为了评估各种缩料 (C/F) 比例的模型性能,在牛肉牛的食中.
主要方法:
- 使用Menke和Steingass的体外发酵方法生成了两个数据集.
- 一个数据集 (60份) 用于在不同的C/F比率 (30:70到90:10) 中进行模型开发.
- 用于模型验证和比较准确性评估,使用了单独的数据集 (10份).
主要成果:
- 两种MLR和BPNN模型都显示了CH4生产和CNCPS碳水化合物成分 (CA,CB1,CB2,CC) 之间的显著关系.
- MLR模型实现了0.91的R平方 (p < 0.0001).
- 一个最佳的BPNN模型 (2个隐藏层神经元) 产生了较高的R平方值0.93 (p < 0.0001),表明具有较低RMSPE和较高CCC的优异预测性能.
结论:
- 无论是MLR还是BPNN模型都适合使用CNCPS碳水化合物组件预测CH4生产.
- 与MLR模型相比,BPNN模型显示出更高的预测准确性.
- 这些发现支持使用像BPNN这样的先进建模技术来更精确地估计牛中的CH4排放量.
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