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Global attractive set for quaternion-valued neural networks with neutral items
Xili Wu1, Zhengwen Tu2, Tao Peng1
1School of Mathematics and Statistics, Chongqing Three Gorges University, Wanzhou, 404100, China.
Abstract:
This paper investigated the global attractive set for quaternion-valued neural networks (QVNNs) with leakage delay, time-varying delay, and neutral items. Based on various basic conditions of activation function, the global attractive set and global exponential attractive set of QVNNs are given combined with novel analytical techniques and Lyapunov theory. The QVNNs are studied by a direct method, without any decomposition. The time delay can be non-differential, which makes the results more pragmatic. Restrictions on the activation function of the neutral item are relaxed. The neutral activation function can be bounded or unbounded, which makes the results more practical. Two simulation examples are given to verify the validity of the theory results.
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