专家模糊系统的性能,用于预测子断奶后的料摄入量
Bruna Campos Amaral1, Marcelo Bahuti2, Tadayuki Yanagi Junior3
1Department of Agricultural Engineering, Agricultural Engineering Graduate Program, Federal University of Lavras, Minas Gerais, Brazil.
Tropical animal health and production
|November 8, 2024
概括
开发模糊推断系统 (FIS) 有助于预测不同温度下的子料摄入量. 使用高斯输入和加权平均模糊化,Sugeno FIS在子管理方面表现最好.
科学领域:
- 动物科学动物科学
- 人工智能的人工智能
- 农业工程 农业工程
背景情况:
- 断奶后的子经历了分离和饮食变化的压力.
- 热不适显著影响子的生产力和福祉.
- 控制温度等环境变量对养殖至关重要.
研究的目的:
- 开发和比较模糊推断系统 (FIS) 来预测新西兰白的料摄入量.
- 在断奶后的不同热条件下评估不同的FIS配置.
- 确定最佳的FIS来支持子管理决策.
主要方法:
- 实验涉及12只子 (30-43天) 在控制的风洞中在20°C,24°C,28°C和32°C的温度下.
- 马姆达尼和苏格诺的FIS被配置为各种输入/输出功能和模糊化方法.
- 使用统计指数对实验数据进行FIS模型的验证.
主要成果:
- 马姆达尼和苏格诺的FIS均为子提供了足够的料摄入量预测.
- 使用高斯输入和加权平均值的Sugeno FIS显示了最小的模拟错误.
- 这种最佳的FIS配置具有很强的概括能力,可以在新的场景中预测料摄入量.
结论:
- 开发的FIS可以作为子养殖场管理的决策支持工具.
- 准确的料摄入预测有助于优化子福利和生产效率.
- 基于FIS预测的气候控制系统的实施确保了最佳的热条件.
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