کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404902 677462 2006 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli
چکیده انگلیسی

This paper presents new theoretical results on the global exponential stability of recurrent neural networks with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that the Cohen–Grossberg neural network is globally exponentially stable, if the absolute value of the input vector exceeds a criterion. As special cases, the Hopfield neural network and the cellular neural network are examined in detail. In addition, it is shown that criteria herein, if partially satisfied, can still be used in combination with existing stability conditions. Simulation results are also discussed in two illustrative examples.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 19, Issue 10, December 2006, Pages 1528–1537
نویسندگان
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