کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947966 | 1439601 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mean-square global exponential stability in Lagrange sense for delayed recurrent neural networks with Markovian switching
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The paper discusses the mean-square global exponential stability in Lagrange sense for delayed recurrent neural networks (DRNNs) with Markovian switching. Two different types of activation functions are considered, which include both bounded and unbounded activation functions. By using the vector Lyapunov function and stochastic analysis technique, we establish two L-operator differential inequalities. By employing the two L-operator differential inequalities and vector Lyapunov functions methods, we provide easily verifiable criteria for Lagrange stability in mean-square sense of DRNNs with Markovian switching. Finally, two numerical examples are given to illustrate the efficiency of the derived results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 226, 22 February 2017, Pages 58-65
Journal: Neurocomputing - Volume 226, 22 February 2017, Pages 58-65
نویسندگان
Qiuxin Chen, Lei Liu, Ailong Wu,