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A MATHEMATICAL FUNCTIONAL DECOMPOSITION APPROACH THROUGH GRANULARITY PARTITION PROCESS IN QUOTIENT SPACE

机译:一种通过粒度分区过程中的数学泛函分解方法

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Over the past decades, several methodologies have coalesced around the functional decomposition and partial solution manipulation techniques. These methodologies take designers through steps that help decompose a design problem and build conceptual solutions based on the intended, product functionality. However, this kind of subjective decomposition restricts solutions of conceptual design within designers' intended the local, rather the whole, solution space. In such cases, the ability for AI-based functional reasoning systems to obtain creative conceptual design solutions is weakened. In this paper, a functional decomposition model based on the domain decomposition theory in quotient space is proposed for carrying out functional decomposition without needing functional reasoning knowledge to support. In this model, the functional decomposition is treated as a granularity partition process in quotient space composed of three variables: the domain granularities, the attribute properties, and the topological structures. The closeness degrees and the attribute properties in fuzzy mathematics are utilized to describe the fuzzy equivalence relations between the granularities in the up-layer and in the lower-layer of the functional hierarchies. According to the order characteristics in the partially sequential quotient space, based on the homomorphism principle, the attribute properties and the topological structures corresponding to the lower-layer of the functional hierarchies are constructed then. Here, the attribute properties are expressed with membership functions pointed to the lower-layer from the up-layer of the functional hierarchies, and the topological structures are expressed with matrixes and the directed function network represent the topological connections among the subfunctions in the lower-layer of the functional hierarchies. Through refining the functional decomposition process step by step, and traversing all tree branches and leaf nodes in the functional decomposition tree, the functional hierarchies are obtained. Since the functional decomposition process not need the user to indicate or manage desired functionality, the model presented in this paper can reduce designers' prejudices or preconceptions on the functional hierarchies, as well as extend the solution space of conceptual design.
机译:在过去的几十年中,几种方法已经围绕功能分解和部分解决方案操纵技术合并。这些方法通过帮助设计人员帮助分解设计问题并基于预期的产品功能构建概念解决方案。然而,这种主观分解限制了设计人员预期的概念设计的解决方案,而是整个解决方案。在这种情况下,基于AI的功能推理系统获得了获得创意概念设计解决方案的能力被削弱。在本文中,提出了一种基于域分解理论的商用空间中的功能分解模型,用于进行功能分解,而不需要功能推理知识来支持。在该模型中,功能分解被视为由三个变量组成的商空间中的粒度分区过程:域粒度,属性属性和拓扑结构。模糊数学中的近度度和属性属性用于描述上层粒度和功能层次的粒度之间的模糊等效关系。根据局部顺序商的顺序特性,基于同性恋原理,然后构建了与功能层次结构的下层对应的属性属性和拓扑结构。这里,属性属性用指向从功能层次结构的上层指向的隶属函数的隶属函数,并且拓扑结构用矩阵表示,并且定向函数网络表示下部的子功能之间的拓扑连接功能层次结构层。通过步骤通过步骤中的功能分解过程,并在功能分解树中遍历所有树枝和叶节点,获得功能层次结构。由于功能分解过程不需要用户指示或管理所需功能,因此本文呈现的模型可以减少设计人员对功能层次结构上的偏见或预先应用,以及扩展概念设计的解决方案空间。

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