## 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.

Translated title of the contribution | A prototype-based rule inference system incorporating linear functions |
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Original language | English |

Pages (from-to) | 2831 - 2853 |

Number of pages | 22 |

Journal | Fuzzy Sets and Systems |

Volume | 161 |

Publication status | Published - Nov 2010 |