A prototype-based rule inference system incorporating linear functions

  • Authors:

  • Yongchuan Tang;Jonathan Lawry

  • Affiliations:

  • College of Computer Science, Zhejiang University, Hangzhou 310027, PR China and Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, UK;Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, UK

  • Venue:
  • Fuzzy Sets and Systems
  • Year:
  • 2010

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Abstract

A calculus of appropriateness measures of linguistic expressions is proposed, which is based on the prototype theory and random set theory interpretation of vague concepts. A prototype-based rule inference system is then introduced to incorporate linguistic labels in the rule antecedents and linear functions in the consequents of rules. And a rule learning algorithm is developed by combining a new clustering algorithm and a conjugate gradient algorithm. The proposed prototype-based inference system is then applied to a number of benchmark prediction problems including a nonlinear two-dimensional surface, the Mackey-Glass time series and the sunspot time-series. Results suggest that the proposed model is very robust and can perform well in high-dimensional noisy data.