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Polynomial state estimation of quaternion-valued fuzzy memristive neural networks
Ruoxia Li1, Ning Shi1, Qun Huang2
1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062 China.
Abstract:
This paper is concerned with global polynomial state estimation issue of memristive neural networks which take quaternion-valued parameters and fuzzy terms into account. First, via drawing support from quaternion-valued norm, an easily analyzable estimation error model is established, which overcomes the complexity brought by the system parameters. Then, a simple feedback controller is designed aiming to obtain the polynomial stability conditions. It is worth noting that, several algebraic forms of polynomial stability criteria for the error system proposed are achieved by applying the quaternion-valued norm, each of these criteria is represented by algebraic inequality, which facilitates validation. Ultimately, illustrative examples are given to show the effectiveness of the theoretical results.
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