通过贝叶斯规范化神经网络进行数值治疗,用于水病模型
Zulqurnain Sabir1, Muhammad Athar Mehmood2, Muhammad Umar3
1Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
Computers in biology and medicine
|February 9, 2025
概括
这项研究引入了一种新的人工神经网络方法,使用贝叶斯规范化,以数值解决水病模型,实现预测的高准确性和可靠性.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 水 (水) 是一种传染性疾病,需要精确的数学模型来控制.
- 了解疾病动态涉及分隔模型对人口进行分类 (易感,接种疫苗,感染等). ) 的情况.
研究的目的:
- 开发和应用一种新的人工神经网络 (ANN) 框架,用于水病模型的数值解决方案.
- 在ANN中评估贝叶斯规范化的有效性,以解决复杂的流行病学模型.
主要方法:
- 构建了一个单一的隐藏层人工神经网络与贝叶斯规范化.
- 数据集是使用Runge-Kutta技术生成的,数据分为培训 (76%),验证 (12%) 和测试 (12%).
- 在ANN架构中采用了一个物流的Sigmoid健身功能和30个神经元.
主要成果:
- 该ANN模型表现出高精度,绝对误差可忽略不计,范围从10-04到10-06.
- 取得了卓越的性能,平均平方误差值介于10-09和10-11之间.
- 通过结果匹配,回归分析和错误组图,模型可靠性得到证实.
结论:
- 提出的人工神经网络框架与贝叶斯规范化提供了一种可靠和准确的方法来解决水疾病模型.
- 这代表了这种特定的ANN架构和优化技术对水流行病学模型的首次应用.
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