Knowledge representation and reasoning using self-learning interval type-2 fuzzy Petri nets and extended TOPSIS

Fuzzy Petri nets (FPNs), which have been extensively used in many fields, are a promising method for knowledge representation and reasoning (KRR). However, there are still some shortcomings in these FPNs. Although there are many improved FPNs, the existing FPNs still have difficulties to ‘embrace’ the inconsistent cognition of experts and individualized features of different systems. In this paper, a new type FPNs are proposed, called self-learning interval type-2 fuzzy Petri nets (SLIT2FPNs). The extended TOPSIS is proposed to determine the optimal alternative, and to collect cognition of experts. An interval numbers ordered weighted averaging operator is introduced to improve the knowledge reasoning capabilities of SLIT2FPNs. Moreover, because of the introduction of state transition algorithm, the model has the ability of self-learning and adjustment according to information dynamics. Finally, two comparison tests are presented to demonstrate the proposed methods. In addition, the applications in aluminum reduction process show that SLIT2FPNs are feasible to ‘embrace’ inconsistent cognition, and to attract individualized features of each reduction cell, and acquire a KRR model with good performance. These facts demonstrate that the methods and ideas proposed in this paper provide a solution for KRR of knowledge automation in the industrial processes.