H<sub>∞</sub> State Estimation for Neural Networks With General Activation Function and Mixed Time-Varying Delays.
basic_science · Level V
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- Record sourced from PubMed, PMID 32822313.
- Also identified by DOI 10.1109/TNNLS.2020.3016120.
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Abstract
This article deals with H<sub>∞</sub> state estimation of neural networks with mixed delays. In order to make full use of delay information, novel delay-product Lyapunov-Krasovskii functional (LKF) by using parameterized delay interval is first constructed. Then, generalized free-weighting-matrix integral inequality is used to estimate the derivative of LKF to reduce the conservatism. Also, a more general activation function is further applied by combining with parameterized delay interval in order to obtain a more accurate estimator model. Finally, sufficient conditions are derived to confirm that the estimation error system is asymptotically stable with a prescribed H<sub>∞</sub> performance. Numerical examples are simulated to show the benefits of our proposed method.