Synchronization Of Fuzzy Neural Networks Research Articles

Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

Fixed/predefined-time synchronization of fuzzy neural networks with stochastic perturbations

In this work, fixed-time (FXT) and predefined-time (PDT) synchronization issue of a type of fuzzy cellular neural networks (FCNNs) with stochastic perturbations is concerned via using some improved FXT stability results. First, we introduced some generalized FXT and PTD stability lemmas for a class of nonlinear stochastic differential equation. Then, based on these novel FXT stability results, we investigated the FXT synchronization of a type of FCNNs with stochastic fluctuations by designing two kinds of simple controllers, which not use a linear term −kiei(t) and provide more accurate settling time (ST) estimations compared to early published literatures. Furthermore, we have also analyzed the PDT synchronization of considered stochastic fuzzy networks via developing several nontrivial PDT control protocols and applying some inequality methods. Finally, we provided one numerical example with its Matlab simulations to show the feasibility of our developed results. We believe that methodology used in this paper can provide some new guidance to FXT and PDT synchronization studies of other nonlinear stochastic systems with or without fuzzy templates.

Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

This paper deals with the synchronization of fuzzy neural networks (FNNs) with time-varying delays. FNNs are more complicated form of neural networks incorporated with fuzzy logics, which provide more powerful performances. Especially, the problem of delayed FNNs's synchronization is of importance in the existence of the network communication. For the synchronization of FNNs with time-varying delays, a novel form of control structure is proposed employing affinely transformed membership functions with memory element. In accordance with affine memory control, appropriate Lyapunov-Krasovskii functional is chosen to design control gain, guaranteeing stability of the systems with delays. Exploiting the more general type of control attributed by affine transformation and memory-type, a novel criterion is derived in forms of linear matrix inequalities (LMIs). As a results, the effectiveness of the proposed control is shown through numerical examples by comparisons with others.