Finite-time state estimation for delayed Hopfield neural networks with Markovian jump

• The finite-time state estimation of delayed Hopfield neural networks with Markovian jump is considered.
• The convergence time can be adjusted by tuning the estimator parameters.
• The results are presented in terms of the linear matrix inequalities.

In this paper, the finite-time state estimation problem of delayed Hopfield neural networks with Markovian jump is investigated. The activation functions are assumed to satisfy the section condition. A discontinuous estimator is designed through available output measurements such that the estimation error converges to the origin in finite time. The conditions that the desired estimator parameters need to satisfy are derived by using the Lyapunov stability theory and inequality technique. These conditions are provided in terms of the linear matrix inequalities. Finally, the effectiveness of the proposed method is illustrated by means of a numerical example.