Passivity Analysis for Discrete-Time Stochastic Markovian Jump Neural Networks With Mixed Time Delays

By Zhengguang Wu, Peng Shi, H. Su, Jian Chu. IEEE Transactions on Neural Networks, 2011

363 citations recorded by the source corpus.

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Citations in the corpus

Stochastic stability and robust stabilization of semi‐Markov jump linear systems (2013, unknown): “Modeled by a set of linear systems with the transitions among the linear systems governed by the Markov chain, the MJLSs can be used to characterize and model different types of systems subject to abrupt changes [1].”
Reliable mixed passive and ℋ∞ filtering for semi‐Markov jump systems with randomly occurring uncertainties and sensor failures (2015, methodology): “As an effective tool, the passivity theory can be employed for analysis and synthesis of linear and nonlinear systems [13, 14].”
Finite-time synchronization of complex networks with nonidentical discontinuous nodes (2013, background): “The switching and jump in these equations are different from those in the Markovian chain [4, 5], since the states of the systems with a Markovian jump are continuous.”
Observer‐based H ∞ control on nonhomogeneous Markov jump systems with nonlinear input (2014, background): “It is a fact that transition probability plays an important role in the performance of such systems, under the assumption that the transition probabilities of MJSs are time invariant, some problems have been studied, such as system analysis [2, 3], stochastic stability and stabilization [4], control”
Stochastic Synchronization of Markovian Jump Neural Networks With Time-Varying Delay Using Sampled Data (2013, methodology): “, [13], [15], [24], [28], [35], [36], and the references therein).”