Structural global parameter identifiability indicates whether one can determine a parameter's value in an ODE model from given inputs and outputs. If a given model has parameters for which there is exactly one value, such parameters are called globally identifiable. Given an ODE model involving not globally identifiable parameters, first we transform the system into one with locally identifiable parameters. As a main contribution of this paper, then we present a procedure for replacing, if possible, the ODE model with an equivalent one that has globally identifiable parameters. We first derive this as an algorithm for one-dimensional ODE models and then reuse this approach for higher-dimensional models.

翻译：结构全局参数可识别性指能否从给定的输入和输出确定常微分方程模型中参数的值。若给定模型存在具有唯一取值的参数，则称此类参数为全局可识别。针对包含非全局可识别参数的常微分方程模型，我们首先将系统转化为具有局部可识别参数的模型。作为本文的核心贡献，我们进一步提出一种方法（在可行情况下）将原常微分方程模型替换为具有全局可识别参数的等价模型。我们首先针对一维常微分方程模型推导出相应算法，随后将该方法推广至高维模型。