Determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. Discrete-time dynamic models are widely used to model biological processes, but it is often difficult to determine these parameters. In this paper, we propose a robust symbolic-numeric approach for parameter estimation in discrete-time models that involve non-algebraic functions such as exp. We illustrate the performance (precision) of our approach by applying our approach to the flour beetle (LPA) model, an archetypal discrete-time model in biology. Unlike optimization-based methods, our algorithm guarantees to find all solutions of the parameter values given time-series data for the measured variables.

翻译：确定动态模型中的未知参数值对于跨学科动力学精确分析至关重要。离散时间动态模型广泛应用于生物过程建模，但这类参数的确定往往面临困难。本文针对包含指数函数等非代数函数的离散时间模型，提出了一种鲁棒的符号-数值参数估计方法。通过将该方法应用于生物学典型离散时间模型——面粉甲虫（LPA）模型，我们展示了该方法的性能（精度）。与基于优化的方法不同，本算法能够保证在给定测量变量时间序列数据的情况下，找到参数值的全部解。