Reduced-order state estimation of complex-valued Markov jump neural networks with time-varying delay and generally uncertain transition rates

In this work, without dividing the complex-valued neutral networks (CVNNs) into a real component and an imaginary component, the partial states of CVNNs with time-varying delay and Markov jump are obtained by a reduced-order state estimator for the first time. And the stochastic stability for the error system is investigated by a state feedback controller. Compared with the full-order state estimation, the computational complexity for the designs of reduced-order state estimation is greatly reduced. And then, the validity and correctness of our theorem is proved by an example.