A novel result on H∞ performance state estimation for Markovian neural networks with time-varying transition rates - Tian, Yufeng; Wang, Zhanshan (Neural computing & applications, 2021)
This paper is concerned with the H ∞ performance state estimation for Markovian neural networks (MNNs) with time-varying transition rates. The time-varying TRs are considered to be in a polytopic sense. To fully consider the time-varying transition rates, a parameter-dependent Lyapunov–Krasovskii functional is constructed. A switched vertices approach is proposed to remove the bound assumptions on the derivative of time-varying parameters in the previous works. To fully facilitate the delay information and the slope information of activation function, some delay-dependent activation function inequalities are constructed under a separation of the estimation error of activation function. Based on these ingredients, a novel result on H ∞ performance state estimation for MNNs is presented in terms of linear matrix inequalities, which is more practical and less conservative. An example demonstrates the effectiveness of proposed approaches.