IEEE Trans Neural Netw Learn Syst. 2016 Apr;27(4):903-9. doi: 10.1109/TNNLS.2015.2425962. Epub 2015 May 11.
In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.
在这篇简报中，研究了具有马尔可夫跳变参数的离散时滞神经网络的混合 H-infinity 和被动性能分析与设计问题，其采用的是 Takagi-Sugeno 模糊模型来表示。本简报的主要目的是设计一个滤波器，通过使用 Lyapunov 方法和随机分析技术，保证增广的马尔可夫跳跃模糊神经网络在均方意义上是稳定的，并满足给定的被动性能指标。应用矩阵分解技术，给出了问题可解的充分条件，这些条件可以用线性矩阵不等式来表示。还给出了一个数值例子来说明所提出技术的有效性。
