'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
This paper is concerned with the dissipativity analysis
and design 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 constructed
based on estimator of Luenberger observer type. Then, applying
piecewise Lyapunov–Krasovskii functional 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 solve the dissipative state estimation problem by using
matrix decomposition approach. Furthermore, to reduce the computational
complexity of the algorithm, a dissipative estimator is
designed to ensure stochastic dissipativity of the error fuzzy jump
neural networks. As a special case, we have also considered the
mixed H∞ and passive analysis of fuzzy jump neural networks.
All criteria can be formulated in terms of linear matrix inequalities.
Finally, two examples are given to show the effectiveness and
potential of the new design techniques.Yingqi Zhang, Peng Shi, Ramesh K. Agarwal, and Yan Sh