Quantized passification of delayed memristor-based neural networks via sliding model control

Publication Type:
Journal Article
Journal of the Franklin Institute, 2020, 357, (6), pp. 3741-3752
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In this paper, quantized passification is investigated for memristive neural networks (MNNs) with time-varying delays via sliding model control. The controller is designed with quantized schemes to reduce the computational complexity via uniform quantization and logarithmic quantizer. By choosing suitable Lyapunov functional and using LMI toolbox, some specific conditions are obtained to make MNN passive. At last, we give an illustrative example to ensure the correctness of the theorem.
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