In this study, we present a methodology of building a hierarchical framework of system modeling by engaging concepts and design methodology of granular computing. We demonstrate that it arises as a result of designing and using locally constructed models to develop a model of a global nature. Two main categories of development of hierarchical models are proposed and discussed. In the first one, given a collection of local models, designed is a granular output space and the ensuing hierarchical model produces information granules of the corresponding type depending upon the depth of the hierarchy of the overall hierarchical structure. The crux of the second category of modeling is about selecting one of the original models and elevating its level of information granularity so that it becomes representative of the entire family of local models. The formation of the most “promising” granular model identified in this way involves mechanisms of allocation of information granularity. The focus of the study is on information granules represented as intervals and fuzzy sets (which in case of type-2 information granules lead to so-called granular intervals and interval-valued fuzzy sets) while the detailed models come as rule-based architectures and neural networks. A series of experiments is presented along with a comparative analysis.

中文翻译：

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层次系统建模


在本研究中，我们提出了一种通过结合粒计算的概念和设计方法来构建系统建模的分层框架的方法。我们证明，它是设计和使用本地构建的模型来开发全球性模型的结果。提出并讨论了层次模型发展的两个主要类别。在第一个模型中，给定本地模型的集合，设计了粒度输出空间，随后的分层模型根据整体分层结构的分层深度产生相应类型的信息颗粒。第二类建模的关键是选择一个原始模型并提高其信息粒度水平，使其能够代表整个局部模型系列。以这种方式识别的最“有前途”的粒度模型的形成涉及信息粒度的分配机制。研究的重点是表示为间隔和模糊集的信息颗粒（在类型 2 信息颗粒的情况下导致所谓的粒度间隔和区间值模糊集），而详细模型则作为基于规则的体系结构和神经网络。提出了一系列实验并进行了比较分析。