INTUITIONISTIC FUZZY HOPFIELD NEURAL NETWORK AND ITS STABILITY
Intuitionistic fuzzy sets (IFSs) are generalization of fuzzy sets by adding an additional attribute parameter called non-membership degree. In this paper, a max-min intuitionistic fuzzy Hopfield neural network (IFHNN) is proposed by combining IFSs with Hopfield neural networks. The stability of IFHNN is investigated. It is shown that for any given weight matrix and any given initial intuitionistic fuzzy pattern, the iteration process of IFHNN converges to a limit cycle. Furthermore, under suitable extra conditions, it converges to a stable point within finite iterations. Finally, a kind of Lyapunov stability of the stable points of IFHNN is proved, which means that if the initial state of the network is close enough to a stable point, then the network states will remain in a small neighborhood of the stable point. These stability results indicate the convergence of memory process of IFHNN. A numerical example is also provided to show the effectiveness of the Lyapunov stability of IFHNN
intuitionistic fuzzy hopfield neural network intuitionistic fuzzy sets limit cycle stable point lyapunov stability