Automatic variable selection and granular adaptation in fuzzyBoolean nets

摘要

In this work the problem of meta-learning, that is, perceivingwhat to learn (which variables, which granularity), is addressed in thecontext of Boolean nets with fuzzy behaviour. Fuzzy relationaloperators, embedded in those neural networks, are defined and the authorshows how they can be used to establish the relevant antecedents as wellas their topology of the network according these concepts and in orderto efficiently learn a given set of rules from experiments is presented