Asynchronous l_2-l_∞ Filtering for Discrete-Time Fuzzy Markov Jump Neural Networks with Unreliable Communication Links

摘要

This paper investigates the problem of l_2-l_∞ asynchronous filtering for a class of discrete-time fuzzy neural networks subject to Markov jump parameters and unreliable communication links. Due to the fact that neural networks possess the nonlinear dynamic characteristic, it is difficult to deal with such a nonlinear characteristic directly, so the Takagi-Sugeno fuzzy model is introduced to approximate the system. Directed against the unreliable communication links, the data packet loss depicted by a stochastic variable with Bernoulli distribution and the signal quantization phenomenon occurring in communication channels are taken into consideration simultaneously. The attention of this paper is mainly centered on devising an asynchronousl_2-l_∞ filter for ensuring the l_2-l_∞ performance of the studied system under asynchronous conditions. Some sufficient conditions for the existence of the asynchronous l_2-l_∞ filter are presented. Finally, a numerical example is given to carry out the simulation experiment, which can verify the effectiveness of the obtained results.