عنوان مقاله فارسی: تجزیه و تحلیل همزمان سازی داده های نمونه برداری شده از شبکه های عصبی مارکوویان با نرخ انتقال به طور کلی ناقص

عنوان مقاله لاتین: Sampled-Data Synchronization Analysis of Markovian Neural Networks With Generally Incomplete Transition Rates

نویسندگان: Huaguang Zhang; Junyi Wang; Zhanshan Wang; Hongjing Liang

تعداد صفحات: 12

سال انتشار: 2017

زبان: لاتین

Abstract:

This paper investigates the problem of sampled-data synchronization for Markovian neural networks with generally incomplete transition rates. Different from traditional Markovian neural networks, each transition rate can be completely unknown or only its estimate value is known in this paper. Compared with most of existing Markovian neural networks, our model is more practical because the transition rates in Markovian processes are difficult to precisely acquire due to the limitations of equipment and the influence of uncertain factors. In addition, the time-dependent Lyapunov-Krasovskii functional is proposed to synchronize drive system and response system. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilize the upper bound of variable sampling interval and the sawtooth structure information of varying input delay. Moreover, the desired sampled-data controllers are obtained. Finally, two examples are provided to illustrate the effectiveness of the proposed method.