Reliable asynchronous sampled-data filtering of T-S fuzzy uncertain delayed neural networks with stochastic switched topologies

This paper investigates the issue of the reliable asynchronous sampled-data filtering of Takagi-Sugeno (T-S) fuzzy delayed neural networks with stochastic intermittent faults, randomly occurring time-varying parameters uncertainties and controller gain fluctuation. The asynchronous phenomenon occurs between the system modes and controller modes. First, in order to reduce the utilization rate of communication bandwidth, a novel alterable sampled-data terminal method is considered via variable sampling rates. Second, based on the fuzzy-model-based control approach, improved reciprocally convex inequality and new parametertime-dependent discontinuous Lyapunov approach, several relaxed conditions are derived and compared with the existing work. Third, the intermittent fault-tolerance scheme is also taken into fully account in designing a reliable asynchronous sampled-data controller, which ensures such that the resultant neural networks is asymptotically stable. Finally, two numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. (C) 2018 Elsevier B.V. All rights reserved.