Fuzzy logic-predicate network
Татьяна Матвеевна Косовская · 2019
In many Artrificial Intelligence problems an investigated object is considered as a set of its elements {ω 1 , . . ., ω t } and is characterized by properties of these elements and relations between them.These properties and relations may be set by predicates p 1 , . . ., p n .The problems appeared with such an approach become NP-complete or NP-hard ones.To decrease the computational complexity of these problems a hierarchical multi-level description of classes was suggested.A logicpredicate recognition network may be constructed according to such a multi-level description.Such a network recognizes only objects which have been presented in the training set, but it may be easily retrained by a new object.After retraining it may change its configuration i.e., the number of levels and the number of nodes in every level.A modification of such a network is offered in this paper.This modification allows to do a fuzzy recognition of a new object and to calculate the degree of certainty that this object or its part belongs to some class of objects.