Extended dissipativity-based synchronization of Markov jump neural networks subject to partially known transition and mode detection information q

This paper addresses the extended dissipativity-based synchronization problem of Markov jump neural networks with partially known probability information by using a detector from the hidden Markov model, where the partially known probability may exist in one of the transition probability matrix and detection probability matrix, or both of them simultaneously. By using such a hidden Markov model, an extended stochastic dissipative synchronization criterion for neural networks with partially known probability information is established, and a novel design method is given with the help of an improved activation function dividing method. Finally, the validity of the proposed approach is demonstrated by two numerical examples.

Automated summary

This paper addresses the extended dissipativity-based synchronization problem of Markov jump neural networks with partially known probability information. It introduces a detector from the hidden Markov model and establishes a criterion for neural networks with partially known probability information. The proposed approach is validated through numerical examples.