Fuzzy Encoded Markov Chains: Overview, Observer Theory, and Applications
本文综述了模糊编码马尔可夫链（FEMC），用于动态系统建模、预测、状态估计和模糊控制，并首次提出了部分可观测FEMC的观测器理论，最后介绍了汽车领域的应用。
This article provides an overview of fuzzy encoded Markov chains (FEMCs), which are finite-state Markov chains applied to transitions between fuzzy sets that encode signal or variable values. FEMCs can be used for modeling of dynamic systems, predicting/forecasting future signal values, for state estimation, and for the development of fuzzy rules for control. Under suitable assumptions, the state possibility distribution can be propagated using FEMC models in a similar manner as the state probability distribution using conventional Markov chain models. The article first discusses FEMC theory, procedures to identify FEMCs from data, and the use of FEMCs for forecasting and control. Then, we introduce, for the first time, observers for partially observable FEMCs. The observer theory is developed and computational approaches are presented. Finally, we briefly review some FEMC applications in the automotive domain.