基于非确定性链的分散集值状态估计
Decentralized set-valued state estimation based on non-deterministic chains
AI总结：
针对接受混合状态机表示的系统，提出一种基于信号空间分解的分散计算框架，通过利用转移关系的结构性质设计分解规则，实现精确状态集恢复并降低计算复杂度。
摘要：
本文考虑了一类接受混合状态机表示的系统的分散集值状态估计与预测的通用计算框架。该分散方案由分布式状态机的合取组成，这些状态机通过外部信号空间的分解来指定。虽然这通常会产生原始整体状态机结果的外近似，但本文通过利用底层转移关系的结构性质，设计了信号空间分解的具体规则，从而恢复了精确的状态集结果。通过应用合适的近似算法，我们表明分散设置下的计算复杂度相比于集中式估计方案可以显著降低。
英文摘要：
A general decentralized computational framework for set-valued state estimation and prediction for the class of systems that accept a hybrid state machine representation is considered in this article. The decentralized scheme consists of a conjunction of distributed state machines that are specified by a decomposition of the external signal space. While this is shown to produce, in general, outer approximations of the outcomes of the original monolithic state machine, here, specific rules for the signal space decomposition are devised by utilizing structural properties of the underyling transition relation, leading to a recovery of the exact state set results. By applying a suitable approximation algorithm, we show that computational complexity in the decentralized setting may thereby essentially reduce as compared to the centralized estimation scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
