PROJECT TITLE :

Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps

ABSTRACT:

This paper is anxious with the dissipativity analysis and style of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and time-varying delays represented by Takagi-Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are first made based mostly on estimator of Luenberger observer type. Then, applying piecewise Lyapunov-Krasovskii useful approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to resolve the dissipative state estimation downside by using matrix decomposition approach. Furthermore, to scale back the computational complexity of the algorithm, a dissipative estimator is designed to confirm stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have a tendency to have conjointly thought-about the mixed H∞ and passive analysis of fuzzy jump neural networks. All criteria will be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.