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Towards utilization of rule base structure to support fuzzy rule interpolation.

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Title: Towards utilization of rule base structure to support fuzzy rule interpolation.
Authors: Jiang, Changhong1 (AUTHOR), Jin, Shangzhu2 (AUTHOR) szjin@cqust.edu.cn, Shang, Changjing1 (AUTHOR) cns@aber.ac.uk, Shen, Qiang1 (AUTHOR)
Source: Expert Systems. Jun2023, Vol. 40 Issue 5, p1-13. 13p.
Subjects: Interpolation, Approximate reasoning, Investigation reports, Texture mapping
Abstract: Fuzzy rule interpolation (FRI) offers a reliable approach for providing an interpretable approximate decision with a sparse rule base, when a new observation does not match any existing rules. As the mainstream application of a fuzzy rule base is to extract valuable approximate information from each individual rules, existing FRI methods typically work by postulating that the more rules used to implement the interpolation the better the reasoning outcomes. Yet, empirical results have shown that using a large number of rules in an FRI process may adversely lead to worsening the accuracy of the inference outcomes, not just degrading efficiency. The objective of this work is to set a firm theoretical foundation for the eventual establishment of a novel FRI approach. It achieves this goal by mapping the structural patterns within a given fuzzy rule base onto a mathematically isomorphic data space, such that the essential information embedded in the original rule base can be effectively captured, represented and analysed. The resulting mathematically mapped patterns enable the production of a theorem that determines the upper limit of the number of rules required to effectively and efficiently perform FRI. The experimental investigations reported herein demonstrate that the number of required rules to perform FRI obeys the theorem discovered in this work. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Fuzzy rule interpolation (FRI) offers a reliable approach for providing an interpretable approximate decision with a sparse rule base, when a new observation does not match any existing rules. As the mainstream application of a fuzzy rule base is to extract valuable approximate information from each individual rules, existing FRI methods typically work by postulating that the more rules used to implement the interpolation the better the reasoning outcomes. Yet, empirical results have shown that using a large number of rules in an FRI process may adversely lead to worsening the accuracy of the inference outcomes, not just degrading efficiency. The objective of this work is to set a firm theoretical foundation for the eventual establishment of a novel FRI approach. It achieves this goal by mapping the structural patterns within a given fuzzy rule base onto a mathematically isomorphic data space, such that the essential information embedded in the original rule base can be effectively captured, represented and analysed. The resulting mathematically mapped patterns enable the production of a theorem that determines the upper limit of the number of rules required to effectively and efficiently perform FRI. The experimental investigations reported herein demonstrate that the number of required rules to perform FRI obeys the theorem discovered in this work. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/exsy.13097
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      – Code: eng
        Text: English
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        PageCount: 13
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    Subjects:
      – SubjectFull: Interpolation
        Type: general
      – SubjectFull: Approximate reasoning
        Type: general
      – SubjectFull: Investigation reports
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      – SubjectFull: Texture mapping
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      – TitleFull: Towards utilization of rule base structure to support fuzzy rule interpolation.
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            NameFull: Jiang, Changhong
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            NameFull: Jin, Shangzhu
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            NameFull: Shang, Changjing
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            – D: 01
              M: 06
              Text: Jun2023
              Type: published
              Y: 2023
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