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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分层系统建模

在这项研究中，我们提出了一种通过引入粒度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。提出并讨论了分层模型开发的两大类。在第一个模型中，给定一组局部模型，设计了一个粒度输出空间，随后的层次模型根据整个层次结构的层次深度产生相应类型的信息粒度。第二类建模的关键是选择一个原始模型并提升其信息粒度级别，使其成为整个本地模型家族的代表。以这种方式确定的最“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区间和模糊集的信息粒度（在类型 2 信息粒度的情况下导致所谓的粒度区间和区间值模糊集），而详细模型则是基于规则的体系结构和神经网络。提供了一系列实验以及比较分析。研究的重点是表示为区间和模糊集的信息粒度（在类型 2 信息粒度的情况下导致所谓的粒度区间和区间值模糊集），而详细模型则是基于规则的体系结构和神经网络。提供了一系列实验以及比较分析。研究的重点是表示为区间和模糊集的信息粒度（在类型 2 信息粒度的情况下导致所谓的粒度区间和区间值模糊集），而详细模型则是基于规则的体系结构和神经网络。提供了一系列实验以及比较分析。