Abstract
AbstractAn H∞ estimation issue for static neural networks (SNNs) is discussed by the research, and the SNN model is improved from the following two aspects: (1) The SNNs contains both bounded and unbounded delays. (2) The nonlinear activation function is bounded and belongs to an interval. Through the construction of the novel Lyapunov–Krasovskii functional (LKF), the state estimator is established. It is also pointed that a gain matrix is acquired by the linear matrix inequality (LMI). Numerical experiments indicate that the method put forward in this study is valid. In addition, the obtained results are characterized by effectiveness and correctness.
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