Asynchronous Filtering for Markov Jump Neural Networks With Quantized Outputs
针对带有时延和量化测量的马尔可夫跳跃神经网络，提出了一种异步滤波器，利用隐马尔可夫模型描述模式异步性，并验证了其在生物网络中的有效性。
In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov-Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly (U, L, V)-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.