IEEE Trans Cybern. 2015 Dec;45(12):2840-52. doi: 10.1109/TCYB.2014.2387203. Epub 2015 Jan 19.
This paper is concerned with the resilient H∞ filtering problem for a class of discrete-time Markov jump neural networks (NNs) with time-varying delays, unideal measurements, and multiplicative noises. The transitions of NNs modes and desired mode-dependent filters are considered to be asynchronous, and a nonhomogeneous mode transition matrix of filters is used to model the asynchronous jumps to different degrees that are also mode-dependent. The unknown time-varying delays are also supposed to be mode-dependent with lower and upper bounds known a priori. The unideal measurements model includes the phenomena of randomly occurring quantization and missing measurements in a unified form. The desired resilient filters are designed such that the filtering error system is stochastically stable with a guaranteed H∞ performance index. A monotonicity is disclosed in filtering performance index as the degree of asynchronous jumps changes. A numerical example is provided to demonstrate the potential and validity of the theoretical results.
本文研究了一类具有时变时滞、非理想测量和乘性噪声的离散时间马尔可夫跳跃神经网络（NNs）的弹性 H∞滤波问题。考虑到 NNs 模式和期望的模式相关滤波器的转换是异步的，并且使用非齐次模式转换矩阵滤波器来模型化不同程度的异步跳跃，这些跳跃也是模式相关的。未知的时变时滞也被假定为与模式相关的，具有先验已知的下限和上限。非理想测量模型以统一的形式包含随机发生的量化和缺失测量的现象。期望的弹性滤波器被设计为使得滤波误差系统在保证 H∞性能指标的情况下具有随机稳定性。随着异步跳跃程度的变化，揭示了滤波性能指标的单调性。提供了一个数值示例来证明理论结果的潜力和有效性。
