کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946961 | 1439561 | 2017 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sampled-data state estimation for delayed memristive neural networks with reaction-diffusion terms: Hardy-Poincarè inequality
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The purpose of this paper is to design a sampled-data state estimator to better estimate the delayed reaction-diffusion memristive neural networks. To tackle with the effect caused by the reaction-diffusion terms, a new agency of Hardy-Poincarè inequality was introduced, which proposed a more accurate estimation. In addition, based on Lyapunov function, robust analysis method, some brand-new solvability criteria are presented, which rest upon the size of the delays, the sampling period as well as the regional feature of the reaction-diffusion region. Finally, two numerical examples are exploited to show the effectiveness of the derived LMI-based conditions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 266, 29 November 2017, Pages 494-505
Journal: Neurocomputing - Volume 266, 29 November 2017, Pages 494-505
نویسندگان
Hongzhi Wei, Ruoxia Li, Chunrong Chen, Zhengwen Tu,