Stability analysis of Takagi–Sugeno stochastic fuzzy Hopfield neural networks with discrete and distributed time varying delays

In this paper, the global stability problem of Takagi–Sugeno (T–S) stochastic fuzzy Hopfield neural networks (TSSFHNNs) with discrete and distributed time varying delays is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSSFHNNs with discrete and distributed time varying delays. Here we choose a generalized Lyapunov functional and introduce a parameterized model transformation with free weighting matrices to it, in order to obtain stability region. In fact, these techniques lead to generalized and less conservative stability condition that guarantee the wide stability region. The proposed stability conditions are demonstrated with numerical examples. Comparison with other stability conditions in the literature shows that our conditions are the more powerful ones to guarantee the widest stability region.