IEEE Trans Cybern. 2020 May;50(5):1900-1909. doi: 10.1109/TCYB.2019.2909748. Epub 2019 Apr 17.
This paper focuses on the state estimator design problem for a switched neural network (SNN) with probabilistic quantized outputs, where the switching process is governed by a sojourn probability. It is assumed that both packet dropouts and signal quantization exist in communication channels. Asynchronous estimator and quantification function are described by two different hidden Markov model between the SNNs and its estimator. To deal with the small uncertain of estimators in a random way, a probabilistic nonfragile state estimator is introduced, where uncertain information is described by the interval type of gain variation. A sufficient condition on mean square stable of the estimation error system is obtained and then the desired estimator is designed. Finally, a simulation result is provided to verify the effectiveness of the proposed design method.
本文针对具有概率量化输出的切换神经网络（SNN）的状态估计器设计问题进行了研究，其中切换过程由逗留概率控制。假设在通信信道中同时存在数据包丢失和信号量化。异步估计器和量化函数由 SNN 与其估计器之间的两个不同的隐马尔可夫模型描述。为了以随机的方式处理估计器的小不确定性，引入了概率非脆弱状态估计器，其中不确定信息由增益变化的区间类型描述。获得了估计误差系统均方稳定的充分条件，然后设计了所需的估计器。最后，提供了一个仿真结果来验证所提出的设计方法的有效性。
