一个计算的随机过程,用于解决流行性呼吸传输系统的解决方案
Najah AbuAli1, Muhammad Bilal Khan2, Zulqurnain Sabir3,4
1College of Information Technology, UAE University, P. O. Box 15551, Al Ain, UAE. najah@uaeu.ac.ae.
Scientific reports
|September 27, 2023
概括
这项研究模拟了一个非线性呼吸传播流行病模型,使用随机尺度结合梯度神经网络 (SCGGNNs). 该SCGGNNs方法准确地以最小的误差模拟疾病动态,验证了其在流行病预测方面的有效性.
科学领域:
- 流行病学 流行病学
- 计算数学 计算数学 计算数学
- 人工智能的人工智能
背景情况:
- 呼吸传播流行病带来了重大的公共卫生挑战.
- 数学模型对于理解和预测流行病动态至关重要.
- 非线性硬普通微分方程经常描述复杂的流行病系统.
研究的目的:
- 在数值上模拟一个非线性呼吸传播流行病系统.
- 提出和应用一种新的随机尺度结合梯度神经网络 (SCGGNNs) 程序.
- 评估SCGGNNs在流行病建模方面的准确性和能力.
主要方法:
- 开发了一个数学模型,其中有四个部分:易感,暴露,感染和恢复.
- 实现了随机尺度结合梯度神经网络 (SCGGNNs) 的实现,具有日志-西格激活函数和二十个隐藏的神经元.
- 使用SCGGNNs的流行病模型的三个不同的病例的数值模拟.
- 通过将结果与数据库解决方案进行比较并分析错误指标来验证SCGGNNs的准确性.
主要成果:
- 该SCGGNNs实现了高精度,可忽略不计的绝对误差在10-06到10-07之间.
- 培训,验证和测试程序成功降低了平均平方误差.
- 数字模拟表明了SCGGNNs对呼吸传播流行病系统的准确性和能力.
- 错误组图,回归值,相关性测试和状态转换证实了模型的准确性.
结论:
- 拟议的随机SCGGNNs程序是模拟非线性呼吸传播流行病系统的有效和准确方法.
- SCGGNNs为流行病预测和分析提供了一种可靠的方法.
- 这项研究验证了SCGGNNs在处理复杂数学模型时的稳定性和精度.
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