基于伴随灵敏度分析的常微分方程参数估计的改进多次打靶算法
A Modified Multiple Shooting Algorithm for Parameter Estimation in ODEs Using Adjoint Sensitivity Analysis
摘要：
为了提高模型的预测能力，需要估计其未知参数。常微分方程模型中的参数估计方法大多收敛范围小或计算量大。另一方面，多重射击的方法介于这两个极端之间。该算法的计算量主要是由于目标函数和约束函数的方向导数的计算。在这里，我们修改{的}多次射击算法使用伴随方法在计算这些衍生物。在文献中，这种方法被认为是计算标量函数梯度的一种更稳定和计算效率更高的方法。一个捕食-食饵系统被用来显示该方法的性能，并提供所有必要的信息，一个成功的和有效的实施。
英文摘要：
To increase the predictive power of a model, one needs to estimate its unknown parameters. Almost all parameter estimation techniques in ordinary differential equation models suffer from either a small convergence region or enormous computational cost. The method of multiple shooting, on the other hand, takes its place in between these two extremes. The computational cost of the algorithm is mostly due to the calculation of directional derivatives of objective and constraint functions. Here we modify { the} multiple shooting algorithm to use the adjoint method in calculating these derivatives. In the literature, this method is known to be a more stable and computationally efficient way of computing gradients of scalar functions. A predator-prey system is used to show the performance of the method and supply all necessary information for a successful and efficient implementation.
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。
