Data structure for a fuzzy machine learning algorithm
Tzung‐Pei Hong, Chai-Ying Lee · 2002
The authors previously (1996) proposed a general learning method for automatically deriving fuzzy if-then rules and membership functions from a set of given training examples by merging the decision tables and membership functions. In this paper, we present an appropriate data structure upon which to base that learning method. A decision array and membership function arrays are used. Depending on the data structure used, procedures for implementing each step in the original learning algorithm efficiently are available.