混合系统的可计算和组合语义

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Davide Bresolin , Pieter Collins , Luca Geretti , Roberto Segala , Tiziano Villa , Sanja Živanović Gonzalez
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引用次数: 0

摘要

混合系统是具有离散和连续混合行为的系统,仅使用离散模型或连续模型都无法忠实地描述其特征。由于混合系统的内在复杂性,非常有必要对其进行组合描述,即把大型系统视为由几个独立研究的较简单子部分组成。此外,由于包括可达性在内的一些问题对于混合系统来说是不可判定的,因此近似决策程序的可用性是另一个重要特征。在本文中,我们为混合系统提出了一个纯粹的行为形式主义,它是组合式的,并支持近似决策程序。该形式主义是抽象的,具有足够的通用性,可以涵盖文献中提出的大多数具体形式主义或语言,或成为它们的语义框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A computable and compositional semantics for hybrid systems

Hybrid Systems are systems having a mixed discrete and continuous behaviour that cannot be characterised faithfully using either discrete or continuous models only.

Due to the intrinsic complexity of hybrid systems, it is highly desirable to describe them compositionally, where large systems are seen as the composition of several simpler subparts that are studied independently. Furthermore, since several problems, including reachability, are undecidable for hybrid systems, the availability of approximate decision procedures is another important feature.

In this paper, we propose a purely behavioural formalism for hybrid systems that is compositional and supports approximate decision procedures. The formalism is abstract and sufficiently general to subsume or be a semantic framework for most of the concrete formalisms or languages proposed in the literature.

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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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