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arXiv 2102.03437nlin.PSmath.OC
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FitzHugh-Nagumo系统的前向灵敏度分析:参数估计

Forward sensitivity analysis of the FitzHugh-Nagumo system: Parameter estimation

Shady E. Ahmed, Omer San, Sivaramakrishnan Lakshmivarahan

摘要:

来自计算神经科学的FitzHugh-Nagumo(FHN)模型在非线性动力学研究中引起了关注,因为它描述了可激发系统的行为并表现出有趣的分叉特性。模型参数的准确估计对于理解解的轨迹如何随时间演化至关重要。为此,我们提供了一个前向灵敏度方法(FSM)的方法来量化的主要模型参数,使用稀疏的测量数据。有限状态机是一种变分同化技术,它将模式的灵敏度与观测值拟合的过程结合起来。分析了有限状态机用于修正FHN模型参数和预测其动态特性的适用性。此外,我们强调了一些观察放置的指导方针,以控制成本函数的形状和改进参数推断迭代。

英文摘要:

The FitzHugh-Nagumo (FHN) model, from computational neuroscience, has attracted attention in nonlinear dynamics studies as it describes the behavior of excitable systems and exhibits interesting bifurcation properties. The accurate estimation of the model parameters is vital to understand how the solution trajectory evolves in time. To this end, we provide a forward sensitivity method (FSM) approach to quantify the main model parameters using sparse measurement data. FSM constitutes a variational data assimilation technique which integrates model sensitivities into the process of fitting the model to the observations. We analyse the applicability of FSM to update the FHN model parameters and predict its dynamical characteristics. Furthermore, we highlight a few guidelines for observations placement to control the shape of the cost functional and improve the parameter inference iterations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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