State estimation for neural networks with random delays and stochastic communication protocol
Fei Zeng, Li Wei Sheng · Systems Science & Control Engineering · 2018
This paper deals with the state estimation problem for delayed neural networks under stochastic communication protocol (SCP). The time delay addressed is random and its probability distribution is known. The information between the sensors and state estimator is transmitted through constrained networks, and the SCP is introduced to determine which sensor could send data at a specific time. Moreover, a Markov chain is applied to described the SCP scheduling in networks. By using the stochastic analysis method, some delay-distribution-dependent conditions are obtained to guarantee the stability of the error dynamics with H∞ performance. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.