橄病控制模型的合成数据集的自主回归外源神经结构,使用分数格林沃尔德-莱特尼科夫解答器
Nabeela Anwar1, Muhammad Asif Zahoor Raja2, Adiqa Kausar Kiani2
1Department of Mathematics, University of Narowal, 50600, Pakistan.
Computers in biology and medicine
|February 6, 2025
概括
这项研究使用智能计算来模拟橄树疾病,分析病原体的传播和开发分数顺序橄病控制 (FO-ODC) 模型. 该方法准确模拟疾病动态,以改善农业管理.
科学领域:
- 农业科学 农业科学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 橄油富含健康脂肪,面临着橄树疾病的威胁.
- 了解疾病动态对于橄业的可持续性至关重要.
- 复杂的生态相互作用会影响橄园中的疾病传播.
研究的目的:
- 分析分数顺序橄病控制 (FO-ODC) 模型的复杂动态.
- 在四种种群的生态框架内调查疾病传播模式.
- 开发和验证用于建模橄植物疾病的智能计算方法.
主要方法:
- 使用非线性自回归外源神经网络与莱文伯格-马奎特方案 (NNLMS).
- 采用了分数格林沃尔德-莱特尼科夫倒数有限差方法来生成合成数据集.
- 分段数据为训练,测试和验证集,用于模型计算和评估.
主要成果:
- 成功计算了非线性FO-ODC模型动态的近似解决方案.
- 通过将结果与参考解决方案相匹配,证明了NNLMS方法的可行性.
- 使用回归分析,错误指标和自相关性验证了方法的可靠性和效率.
结论:
- 智能计算范式为分析分数顺序疾病控制模型提供了一个强大的框架.
- 这项研究提供了一个可靠的计算工具,用于理解和管理橄树疾病.
- 这项研究通过先进的建模技术,有助于保护橄油行业.
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