用ITSO优化堆叠合奏学习组合评估乳牛干物质摄入量的研究
Shuairan Wang1, Ting Long1, Xiaoli Wei2
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Animals : an open access journal from MDPI
|February 27, 2026
概括
一个与ITSO优化的新堆叠组合模型准确评估奶牛干物质摄入量 (DMI). 这种精密料工具提高了农场的效率,并支持可持续的乳制品生产实践.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 动物营养 动物营养
背景情况:
- 干物质摄入量 (DMI) 对于奶牛的营养和生产效率至关重要.
- 传统的DMI测量方法昂贵而复杂.
- 现有的神经网络模型用于DMI评估可能是计算密集的.
研究的目的:
- 开发一个具有成本效益和准确的模型来评估奶牛DMI.
- 使用先进的元启发算法优化模型参数.
- 提高奶牛养殖中的精准养策略.
主要方法:
- 为DMI评估提出了一个堆叠集体学习模型.
- 模型参数使用改进的金枪鱼群优化 (ITSO) 算法进行了优化.
- 为了提高效率,ITSO结合了Sine-Logistic混乱映射,Levy飞行和高斯随机步行.
主要成果:
- 在ITSO优化的堆叠模型实现了高的DMI评估准确性95.84%.
- 拟议的模型显著优于其他机器学习模型 (SVR,RF,DT,GBR,ETR,AdaBoost).
- 输入变量包括牛的体重,活动指标和料比率.
结论:
- ITSO优化的堆叠模型为DMI评估提供了强大而准确的解决方案.
- 这种方法支持精密养,从而实现最佳的养策略.
- 该研究有助于提高奶牛场的效率,并促进可持续的做法.
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