کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404597 677439 2009 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Stability analysis of static recurrent neural networks using delay-partitioning and projection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Stability analysis of static recurrent neural networks using delay-partitioning and projection
چکیده انگلیسی

This paper introduces an effective approach to studying the stability of recurrent neural networks with a time-invariant delay. By employing a new Lyapunov–Krasovskii functional form based on delay partitioning, novel delay-dependent stability criteria are established to guarantee the global asymptotic stability of static neural networks. These conditions are expressed in the framework of linear matrix inequalities, which can be verified easily by means of standard software. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Finally, two examples are given to show the effectiveness of the theoretical results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 22, Issue 4, May 2009, Pages 343–347
نویسندگان
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