Neural network based asynchronous synchronization for fuzzy hidden Markov jump complex dynamical networks

This paper investigates the drive-response synchronization problem of Takagi-Sugeno fuzzy hidden Markov jump complex dynamical networks. More precisely, a novel asynchronous synchronization control strategy is developed for coping with mismatched hidden jumping modes. Furthermore, the neural network is adopted with online learning laws for unknown function approximation. By taking advantage of Lyapunov method, sufficient conditions are established to ensure mean-square synchronization performance with disturbances. Based on the synchronization criterion, asynchronous controller gains are designed in terms of linear matrix inequalities. An illustrative example is finally given to validate the effectiveness of the proposed synchronization techniques.

Automated summary

This paper investigates the synchronization problem of Takagi-Sugeno fuzzy hidden Markov jump complex dynamical networks, proposing an asynchronous synchronization control strategy and utilizing neural networks for unknown function approximation. Sufficient conditions are established for mean-square synchronization performance with disturbances using Lyapunov method, and asynchronous controller gains are designed based on linear matrix inequalities. An illustrative example is provided to demonstrate the effectiveness of the proposed synchronization techniques.