State estimation for neural networks with jumping interval weight matrices and transmission delays

This work addresses the state estimation problem for the neural networks possessing Markovian jump weight matrices and transmission delays. Markovian jump interval matrices are introduced to model the uncertainty of the connections among neurons, and the polytopic model is used to describe the uncertainty of the Markov transition matrix. The mode-dependent transmission delays are introduced to describe an unideal communication channel. A sufficient condition of the stochastic stability and the strict-(Q, S, R) dissipative performance is derived for the augmented system. Mode-dependent estimator gains are then designed. At last, an example is employed to illustrate the obtained results.