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适应性神经模糊推断系统用于改善乳腺炎的分类和诊断

Javad Shirani Shamsabadi¹, Saeid Ansari Mahyari², Mostafa Ghaderi-Zefrehei³

¹Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran.

Scientific reports

|July 2, 2025

概括

这项研究比较了三种适应性神经模糊推断系统 (ANFIS) 用于分类乳牛乳腺炎. 梯度下降ANFIS与皮尔森相关性显示在检测阴茎感染方面表现最好.

科学领域:

乳牛的健康状况乳牛的健康状况
农业中的计算智能
软计算应用软计算应用程序

背景情况:

乳腺炎显著影响牛奶质量和奶牛场经济.
准确的乳腺炎检测对于有效的管理和资源分配至关重要.
模糊逻辑模型为管理乳制品数据中的不确定性提供了潜力.

研究的目的:

为了比较三个适应性神经模糊推断系统 (ANFIS) 在荷尔斯坦乳牛中乳腺炎分类的性能.
为了评估梯度下降 (GD) -ANFIS,粒子群集优化 (PSO) -ANFIS和遗传算法 (GA) -ANFIS的有效性.
评估特征减少技术 (皮尔森相关性,PCA) 和低样本对模型性能的影响.

主要方法:

采用了三种ANFIS方法:GD-ANFIS,PSO-ANFIS和GA-ANFIS.使用了三种ANFIS方法:GD-ANFIS,PSO-ANFIS和GA-ANFIS.
利用皮尔森相关性和主要组件分析来减少特征.
应用了一个低样本算法来解决数据集中的类不平衡.

主要成果:

与皮尔森方法相结合的GD-ANFIS模型在乳腺炎分类方面表现优异,与PSO-ANFIS和GA-ANFIS相比.
关键性能指标包括准确性,精度,回忆和F1分数被用于评估.
虽然GD-ANFIS显示出强的结果,但由于多个标准的相互作用,最终的模型选择具有挑战性.

结论:

该研究强调了ANFIS的潜力,特别是GD-ANFIS与Pearson特征减少,以改善乳牛乳腺炎的检测.
这些发现有助于软计算在精密奶牛养殖和乳健康管理方面的进步.
提供了 MATLAB 代码,用于在精密乳制品生产中可复制性和移动应用的潜在开发.

关键词:

安菲斯 (ANFIS) 是一个字母.阶级不平衡造成的不平衡乳牛牛奶牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛功能减少功能减少模糊的逻辑模糊的逻辑乳腺炎是一种乳腺炎.

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