期刊
NEURAL PROCESSING LETTERS
卷 53, 期 2, 页码 983-1000出版社
SPRINGER
DOI: 10.1007/s11063-020-10422-5
关键词
Complex-valued neural networks; Exponential stabilization; Looped functional; Linear matrix inequality (LMI); Time-varying delay
资金
- National Science Foundation of China [61973199, 61573008, 61773207]
- Shandong University of Science and Technology Research Fund [2018TDJH101]
This paper addresses the problem of quantized sampled-data control for CVNNs with time-varying delay under the assumption that only quantized measurements are transmitted to the controller. By utilizing stability theory and estimation techniques, a conservative stability criterion is obtained and a corresponding controller is designed, with simulation results demonstrating the effectiveness of the criteria.
This paper addresses the problem of quantized sampled-data control for CVNNs with time-varying delay under the assumption that only quantized measurements are transmitted to the controller. Based on the discrete-time Lyapunov stability theory, reciprocally convex approach, a sector bound approach, and some estimation techniques, a reduced conservative stabilization criterion is obtained to guarantee the exponential stabilization of the considered CVNNs. The desired quantized sampled-data controller is designed via converting the complex-valued linear matrix inequality into real-valued ones. The effectiveness of the derived criteria are shown via an illustrative simulation example.
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