AI 中文总结
针对空间填充输入设计依赖假设模型、鲁棒性不足的问题，提出基于随机优化的鲁棒空间填充输入设计算法，通过对模型类总体的最优性准则取期望并结合随机近似技术优化，仿真验证其有效性。
AI 中文摘要
空间填充输入设计方法会在系统模型的特征空间中生成所谓的空间填充数据集，该设计方法适用于广泛类别的模型结构，可选择多种信号，并通过最优性准则将信息度量纳入信号设计。但在信号设计过程中，需要已知假设模型的信息，若真实系统与假设的系统模型存在显著差异，设计出的信号性能会远低于最优水平。本文提出一种鲁棒空间填充输入设计算法，可为整个模型类生成空间填充数据集，该算法会对模型类总体上的最优性准则取期望，并采用随机近似技术优化该鲁棒准则，仿真示例验证了所提算法的有效性。
英文摘要
The space-filling input design approach generates a so-called space-filling dataset in the feature space of the system model. The design method is applicable on a broad class of model structures with wide selection of signals and also incorporates information measures through optimality criteria into the signal design. However, during the signal design, knowledge of a hypothesized model is required. The designed signal can perform far from the optimal if the true system is significantly different from the hypothesized system model. This paper proposes a robust space-filling input design algorithm that can generate a space-filling dataset for an entire class of models. The proposed algorithm takes the expectation of an optimality criterion over the population of the model class, and a stochastic approximation technique is employed to optimize this robust criteria. The efficiency of the proposed algorithm is demonstrated in a simulation example.
