Yan Huaicheng, Zhang Hao, Yang Fuwen, Zhan Xisheng, Peng Chen
IEEE Trans Neural Netw Learn Syst. 2018 Aug;29(8):3588-3598. doi: 10.1109/TNNLS.2017.2732240. Epub 2017 Aug 18.
This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.
本文研究了一类具有事件触发机制、异步跳变和衰落信道的马尔可夫跳变离散时间神经网络（NNs）的保性能控制问题。引入马尔可夫跳变神经网络是为了使其更贴近实际，其中神经网络的模式和保性能控制器由两个相互独立的马尔可夫链决定。考虑了异步现象，这增加了设计所需的模式依赖控制器的难度。事件触发机制是通过在数据传输过程中将相对测量误差与上一次触发状态进行比较来设计的，用于消除不必要的传输并降低网络能耗。此外，考虑了无线网络中信号反射和阴影对信号衰落的影响，采用新型莱斯衰落模型对其进行建模。通过线性矩阵不等式，得到了一些新的充分条件，以保证在设计的跳变状态反馈控制律下闭环系统达到指定的代价值。最后，给出了一些仿真结果以说明所提方法的有效性。
