在具有少量传感器的随机离散事件系统中识别不可观察的行为
Rubén Santillán-Mosquera1, Mariela Muñoz-Añasco1
1Facultad de Ingeniería Electrónica y Telecomunicaciones, Grupo de Automática, Universidad Del Cauca, Popayán, Cauca, Colombia.
MethodsX
|August 28, 2023
概括
本研究引入了一种通过识别定时模型来建模随机离散事件系统 (DDES) 的新方法. 它使用事件序列和解释Petri网推断可观测和不可观测的系统行为.
科学领域:
- 系统工程 系统工程
- 计算机科学 计算机科学
- 控制理论 控制理论
背景情况:
- 动态离散事件系统 (DDES) 对于模拟具有异步事件的复杂系统至关重要.
- 现有的建模方法根据系统类型和目标而异.
- 对可观测和不可观测行为的准确建模对于系统分析和控制至关重要.
研究的目的:
- 开发一种方法来识别随机离散事件系统的时间模型.
- 推断可观测和不可观测的系统行为.
- 为了增强具有有限传感器数据的系统的建模能力.
主要方法:
- 在闭环系统运行期间观察事件序列及其时间.
- 使用随机时间解释的彼得里网 (st-IPN) 建模可观察的行为.
- 通过观察和生成语言之间的语言投影推断不可观察的行为.
主要成果:
- 成功确定了包含可观测和不可观测动态的时间模型.
- 证明了即使有有限的传感器数据,也能够推断出不可观察的行为.
- 开发的方法适用于广泛的事件序列.
结论:
- 拟议的方法有效地利用定时事件对随机离散事件系统进行建模.
- 它在推断不可观察的系统行为方面取得了重大进展.
- 这种方法对传感器信息稀少的系统有价值,增强了它们的整体建模和分析.
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