论文标题
通过伴随灵敏度分析，用于ODE的参数估计的修改了多个拍摄算法
A Modified Multiple Shooting Algorithm for Parameter Estimation in ODEs Using Adjoint Sensitivity Analysis
论文作者
论文摘要
为了增加模型的预测能力，需要估计其未知参数。普通微分方程模型中的几乎所有参数估计技术都遭受一个较小的收敛区域或巨大的计算成本。另一方面，多次拍摄的方法在这两个极端之间。该算法的计算成本主要是由于计算客观和约束函数的定向导数。在这里，我们修改{}多个拍摄算法以使用伴随方法计算这些衍生物。在文献中，已知该方法是计算标量函数梯度的一种更稳定和计算上的方法。捕食者捕集系统用于显示该方法的性能，并提供所有必要的信息，以实现成功，有效的实施。
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.
