The Learning System of Intuitionistic Optimum Based on Hesitancy Set

The paper describes intuitionistic optimum models for attribute selection of optimum and non-optimum and deals the intuitionistic learning system of analyzing sub-optimum with the degree of knowledge understanding and credit degree of intuitionistic feature. Attribute selection of optimum and non-optimum is performed under both supervised and unsupervised learning. The task of non-optimum analysis is done using a knowledge-based system under supervised learning. The methodology for attribute selection involves minimization of hesitancy set evaluation indices, defined in term of hesitancy function, in connectionist framework.