TY - JOUR
T1 - Hierarchical System Modeling
AU - Al-Hmouz, Rami
AU - Pedrycz, Witold
AU - Balamash, Abdullah Saeed
AU - Morfeq, Ali
N1 - Funding Information:
Manuscript received July 28, 2016; revised September 29, 2016; accepted November 29, 2016. Date of publication January 9, 2017; date of current version February 1, 2018. This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant 135-689-D1435.
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2018/2
Y1 - 2018/2
N2 - 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.
AB - 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.
KW - Fuzzy rule-based models
KW - granular computing
KW - hierarchical models
KW - information fusion
KW - information granules of higher type
KW - principle of justifiable granularity
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U2 - 10.1109/TFUZZ.2017.2649581
DO - 10.1109/TFUZZ.2017.2649581
M3 - Article
AN - SCOPUS:85041458032
SN - 1063-6706
VL - 26
SP - 258
EP - 269
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 1
M1 - 7809139
ER -