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生物启发的启发式计算方案用于人类肝脏非线性模型.

Zulqurnain Sabir1, Salem Ben Said2, Qasem Al-Mdallal2

  • 1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.

Heliyon
|April 15, 2024
PubMed
概括

一种新的生物灵感计算方法有效地模拟了人类肝脏的非线性动态. 这种人工神经网络 (ANN) 方法,通过遗传算法 (GA) 和内部点 (IP) 方法进行优化,提供准确可靠的解决方案.

关键词:
遗传算法 遗传算法 遗传算法启发式 启发式是一种启发式的启发式.内部点 内部点 内部点肝脏模型 肝脏模型模拟的模拟方法转移函数 转移函数 是一个转移函数.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物启发的计算方法
  • 生物医学建模模型

背景情况:

  • 人类肝脏表现出复杂的非线性行为,对生理学理解至关重要.
  • 现有的模型可能缺乏准确性来捕捉这些复杂的动态.

研究的目的:

  • 开发一种生物启发的启发式计算方法,用于解决非线性人类肝脏模型.
  • 验证拟议计算方法的准确性和可靠性.

主要方法:

  • 使用基于人工神经网络 (ANN) 的随机计算.
  • 使用混合优化方案,将遗传算法 (GA) 和内部点 (IP) 方法 (GAIP) 结合起来.
  • 从肝脏模型的微分形式设计一个健身函数,并用GAIP优化它.

主要成果:

  • 拟议的GAIP解决方案表现出高精度,与参考的亚当斯方案解决方案密切一致.
  • 低绝对误差值证实了开发的计算解决方案的有效性和价值.
  • 统计分析进一步验证了非线性肝脏建模方法的可靠性.

结论:

  • 生物启发的GAIP方法为解决非线性人类肝脏模型提供了准确可靠的方法.
  • 这种利用ANN和混合优化的计算策略,有望推动生物医学模拟的发展.