Abstract 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.

中文翻译：

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使用伴随灵敏度分析的 ODE 中参数估计的改进多重射击算法

摘要 为了提高模型的预测能力，需要对其未知参数进行估计。几乎所有常微分方程模型中的参数估计技术要么收敛区域小，要么计算成本巨大。另一方面，多重射击的方法介于这两个极端之间。该算法的计算成本主要是由于计算目标函数和约束函数的方向导数。在这里，我们修改了多次射击算法以使用伴随方法来计算这些导数。在文献中，这种方法被认为是计算标量函数梯度的更稳定和计算效率更高的方法。