A learning methodology in uncertain and imprecise environments
Antonio González · International Journal of Intelligent Systems · 1995
A step-by-step methodology for learning fuzzy rules is presented. This methodology tries to be general enough to give a framework within which different learning methods in an environment of uncertainty and imprecision could be developed. the final product will always be an uncertainty distribution on the different rules representing the behavior of the system. the particular uncertainty distribution depends on the concrete functions selected in each step of the process. the learning approach can also be considered as a way to construct uncertainty measures from data. © 1995 John Wiley & Sons, Inc.