Inferring dynamic system models from observed time course data is very challenging compared to static system identification tasks. Dynamic system models are complicated to infer due to the underlying large search space and high computational cost for simulation and verification. In this research we aim to infer both the structure and parameters of a dynamic system simultaneously by particle swarm optimization (PSO) improved by efficient stratified sampling approaches. More specifically, we enhance PSO with two modern stratified sampling techniques, i.e., Latin hyper cube sampling (LHS) and Latin hyper cube multi dimensional uniformity (LHSMDU). We propose and evaluate two novel swarm-inspired algorithms, LHS-PSO and LHSMDU-PSO, which can be used particularly to learn the model structure and parameters of complex dynamic systems efficiently. The performance of LHS-PSO and LHSMDU-PSO is further compared with the original PSO and genetic algorithm (GA). We chose real-world cancer biological model called Kinetochores to asses the learning performance of LHSMDU-PSO and LHS-PSO in comparison with GA and the original PSO. The experimental results show that LHSMDU-PSO can find promising models with reasonable parameters and structure, and it outperforms LHS-PSO, PSO, and GA in our experiments.

中文翻译：

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使用群体智能方法同时推断动态系统模型的结构和参数

与静态系统识别任务相比，从观察到的时间过程数据推断动态系统模型非常具有挑战性。由于潜在的大搜索空间以及用于仿真和验证的高计算成本，因此动态系统模型的推断很复杂。在这项研究中，我们旨在通过有效分层采样方法改进的粒子群优化（PSO）同时推断动态系统的结构和参数。更具体地说，我们使用两种现代的分层采样技术来增强PSO，即拉丁超立方体采样（LHS）和拉丁超立方体多维均匀性（LHSMDU）。我们提出并评估了两种新颖的群体启发算法LHS-PSO和LHSMDU-PSO，它们可用于有效地学习复杂动态系统的模型结构和参数。将LHS-PSO和LHSMDU-PSO的性能与原始PSO和遗传算法（GA）进行了进一步比较。我们选择了称为Kinetochores的现实世界癌症生物学模型来评估LHSMDU-PSO和LHS-PSO与GA和原始PSO相比的学习性能。实验结果表明，LHSMDU-PSO可以找到具有合理参数和结构的有希望的模型，并且在我们的实验中优于LHS-PSO，PSO和GA。