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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.

|April 15, 2024

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.

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.

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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