Optimal allocation of information granularity in system modeling through the maximization of information specificity: A development of granular input space |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, Canada;2. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;4. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, PR China;1. Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor, Malaysia;2. School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia;1. A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India;2. Department of MCA, Future Institute of Engineering & Management, Kolkata, India;3. Department of Computer Applications, National Institute of Technology, Durgapur, India;4. Department of Computer Science & Engineering, University of Calcutta, Kolkata, India;5. Department of Computer Science, Winona State University, MN, USA;1. School of Mathematical Sciences, Fudan University, 200433, China;2. School of Statistics, Dongbei University of Finance and Economics, 116025, China;3. School of Mathematics and Statistics, Lanzhou University, 730000, China;1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China;2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;3. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China |
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| Abstract: | In this study, we introduce a concept of a granular input space in system modeling, in particular in fuzzy rule-based modeling. The underlying problem can be succinctly formulated in the following way: given is a numeric model, develop an efficient way of forming granular input variables so that the corresponding granular outputs of the model achieve the highest level of specificity. The rationale behind the formulation of the problem is offered along with several illustrative examples. In conjunction with the underlying idea, developed is an algorithmic framework supporting an optimization of the specificity of the model exposed to granular inputs (data). It is dwelled upon one of the principles of Granular Computing, namely an optimal allocation of information granularity. For illustrative purposes, the study is focused on information granules formalized in terms of intervals (however the proposed approach becomes equally relevant for other formalism of information granules). Some comparative analysis with the existing idea of global sensitivity analysis is also carried out by contrasting the essential differences among the two approaches and analyzing the results of computational experiments. |
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| Keywords: | Fuzzy models Information granules Granular input space Global sensitivity Interval arithmetic |
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