Rough-fuzzy rule interpolation

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• 作者：Chengyuan Chen^a ; ^{ccy@cqust.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Neil Mac Parthalá ; in^b ; ^{ncm@aber.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Ying Li^c ; ^{lybyp@nwpu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Chris Price^b ; ^{cjp@aber.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Chai Quek^d ; ^{ashcquek@ntu.edu.sg" class="auth_mail" title="E-mail the corresponding author} ; Qiang Shen ; ^b ; ^{qqs@aber.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fuzzy rule interpolation ; Rough-fuzzy sets ; Transformation-based interpolation
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：10 July 2016
• 年：2016
• 卷：351
• 期：Complete
• 页码：1-17
• 全文大小：1028 K

文摘

Fuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a useful conclusion. Unfortunately, very little of the existing work on fuzzy rule interpolation can conjunctively handle more than one form of uncertainty in the rules or observations. In particular, the difficulty in defining the required precise-valued membership functions for the fuzzy sets that are used in conventional fuzzy rule interpolation techniques significantly restricts their application. In this paper, a novel rough-fuzzy approach is proposed in an attempt to address such difficulties. The proposed approach allows the representation, handling and utilisation of different levels of uncertainty in knowledge. This allows transformation-based fuzzy rule interpolation techniques to model and harness additional uncertain information in order to implement an effective fuzzy interpolative reasoning system. Final conclusions are derived by performing rough-fuzzy interpolation over this representation. The effectiveness of the approach is illustrated by a practical application to the prediction of diarrhoeal disease rates in remote villages. It is further evaluated against a range of other benchmark case studies. The experimental results confirm the efficacy of the proposed work.