Passivity of switched recurrent neural networks with time-varying delays

Jie Lian, Jun Wang

Результат исследований: Вклад в журналСтатьярецензирование

111 Цитирования (Scopus)


This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.

Язык оригиналаАнглийский
Номер статьи7001700
Страницы (с-по)357-366
Число страниц10
ЖурналIEEE Transactions on Neural Networks and Learning Systems
Номер выпуска2
СостояниеОпубликовано - 1 февр. 2015
Опубликовано для внешнего пользованияДа


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