Extended dissipative filtering for Markov jump BAM inertial neural networks under weighted try-once-discard protocol

Abstract This paper investigates the problem of extended dissipative filtering for bidirectional associative memory inertial neural networks, where the Markov chain is introduced to describe the switching characteristic in the structure and parameters. Moreover, considering the limited network bandwidth, the weighted try-once-discard protocol, as a significant scheduling mechanism in determining which nodes can be accessed between the sensor nodes and the filter, is employed to avoid the data collisions under the constrained communication channel. The objective of the paper is to develop a filter which can ensure that the filtering error system is stochastically stable with extended dissipative performance. Based on the Lyapunov function and an improved decoupling approach, a set of sufficient conditions satisfying the above objective are derived, and the filter gains are obtained. Finally, an illustrative example is employed to verify the validity of the proposed method.