کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4974601 | 1365540 | 2016 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Delay-dependent stability and dissipativity analysis of generalized neural networks with Markovian jump parameters and two delay components
ترجمه فارسی عنوان
تجزیه و تحلیل پایداری و عدم تحرک وابسته به تاخیر از شبکه های عصبی تعمیم یافته با پارامترهای پرش مارکوویچ و دو عامل تاخیر
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
This paper focuses on the problem of delay-dependent stability and dissipativity analysis of generalized neural networks (GNNs) with Markovian jump parameters and two delay components. By constructing novel augmented Lyapunov-Krasovskii functional (LKF), using free-matrix-based inequality to estimate the derivative of Lyapunov function and employing the reciprocally convex approach to consider the relationship between the time-varying delay and its interval, some improved delay-dependent stability criteria and dissipativity criteria are established in terms of linear matrix inequalities. Moreover, the obtained criteria is extended to analyze the stability analysis of GNNs with two delay components and the passivity analysis of GNNs with one delay. Numerical examples are given to show the effectiveness and the significant improvement of the proposed methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Franklin Institute - Volume 353, Issue 9, June 2016, Pages 2137-2158
Journal: Journal of the Franklin Institute - Volume 353, Issue 9, June 2016, Pages 2137-2158
نویسندگان
Guoliang Chen, Jianwei Xia, Guangming Zhuang,