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Extended dissipative analysis of delayed stochastic Markovian jump neural networks

In this brief, an extended dissipativity analysis is developed for a delayed stochastic Markovian jump neural networks(DSMNNs). Based on the concept of the extended dissipative, H<sub>∞</sub>, L<sub>2</sub>-L<sub>∞</sub>, the passive and dissipative performance are solved by adjusting the weighting matrices in a new performance index. In addition, two different methods are applied to deal with the stochastic cross terms containing Ito integral.