Generating the Structure of a Fuzzy Rule under Uncertainty

TL;DR

This paper introduces a method for identifying the minimal structure of fuzzy rules under uncertainty using an ATMS and an algorithm, demonstrated on the Iris plant classification dataset.

Contribution

It presents a novel algorithm for simultaneously determining the minimal rule structure and identification parameters in fuzzy models under uncertainty.

Findings

Successfully identified minimal fuzzy rule structures

Applied method to classify Iris plant data

Demonstrated effectiveness in uncertain environments

Abstract

The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy model. For this purpose, an ATMS shall be used (Zurita 1994). An algorithm obtaining the identification of the structure will be suggested (Castro 1995). The minimal structure of the rule (with respect to the number of variables that must appear in the rule) will be found by this algorithm. Furthermore, the identification parameters shall be obtained simultaneously. The proposed method shall be applied for classification to an example. The {em Iris Plant Database} shall be learnt for all three kinds of plants.