Generating the Structure of a Fuzzy Rule under Uncertainty
This work addresses rule structure identification in fuzzy models for classification tasks, but appears incremental as it builds on prior methods like ATMS.
The paper tackles the problem of identifying the minimal structure of a fuzzy rule under uncertainty, using an ATMS-based algorithm to simultaneously determine rule structure and parameters, and applies it to classify the Iris Plant Database for all three plant types.
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.