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Fuzzy classification context for the responsive and formal design process

机译：响应式和正式设计过程的模糊分类上下文

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This paper presents an application of a fuzzy relation in system modeling (from requirements) to be used for a Systems Engineering (SE) methodology. We define fuzzy classifications (models for distributed systems), extract component and system theories (sets of logical expressions), and ensure consistency of requirements for the Responsive and Formal Design (RFD) Process. The RFD process is a SE methodology that relates a set of requirements, associated models, simulations, and the relationship between them, by integrating Model-Based Systems Engineering (MBSE) to manage system modeling complexity with formal methods to ensure that designs are verifiably correct against their requirements. To translate informal requirements to logical expressions in the RFD process, we first model requirements using a 3-tuple structure called a classification formulated from Barwise and Seligman's channel theory. A classification consists of "tokens" (observed situations) and "types" (situation features) and a binary relation classifying tokens with types. However, classifying tokens using types as present (represented as `1') or absent (represented as `0') as used in channel theory is not always possible (since it involves vagueness and imprecission) and the representation lacks expressiveness to reason about relations among such types (vague situation features). Hence, a binary classification doesn't capture uncertainity. In this paper, we consider a degree of truth in the relation between tokens and types to define a fuzzy classification. We then develop an algorithm that extracts a theory from a fuzzy classification. This helps in formal proof for checking consistency (no contradiction) and deducing to requirements (verifying properties). We demonstrate our development using three small satellites measurement system whose goal is to image the colorful auroral ovals seen around Earth's magnetic poles.
机译：本文提出了一种模糊关系在系统建模中的应用（根据需求），可用于系统工程（SE）方法。我们定义模糊分类（用于分布式系统的模型），提取组件和系统理论（逻辑表达式集），并确保响应式和形式化设计（RFD）流程的要求的一致性。 RFD流程是一种SE方法，通过将基于模型的系统工程（MBSE）集成在一起以形式化的方法来管理系统建模的复杂性，从而确保设计可验证的正确性，从而将一组需求，关联的模型，仿真以及它们之间的关系关联起来。违背他们的要求。为了在RFD流程中将非正式需求转换为逻辑表达式，我们首先使用3元组结构（通过Barwise和Seligman渠道理论制定的分类）对需求进行建模。分类由“令牌”（观察到的情况）和“类型”（情况特征）以及将令牌与类型分类的二进制关系组成。但是，使用渠道理论中使用的当前类型（表示为“ 1”）或不存在类型（表示为“ 0”）对令牌进行分类并非总是可能的（因为它涉及模糊性和不确定性），并且表示形式缺乏表达关系推理的能力。在这些类型之间（模糊的情况特征）。因此，二元分类不会捕获不确定性。在本文中，我们考虑了标记和类型之间的关系的真实程度，以定义模糊分类。然后，我们开发一种从模糊分类中提取理论的算法。这有助于形式证明，以检查一致性（无矛盾）并推导出要项（验证属性）。我们使用三个小型卫星测量系统演示了我们的发展，该系统的目的是对地球磁极周围看到的彩色极光椭圆进行成像。

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