IEEE Trans Neural Netw Learn Syst. 2018 Apr;29(4):845-855. doi: 10.1109/TNNLS.2016.2636325. Epub 2017 Jan 24.
This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.
本文研究了具有参数不确定性和随机分布时滞的耦合神经网络的鲁棒状态估计问题，其中采用多胞模型来描述参数不确定性。引入一组具有不同随机特性的伯努利过程来对分布时滞的随机发生进行建模。提出了基于局部耦合结构的新型状态估计器，以充分利用耦合信息。基于 Kronecker 积获得了增广估计误差系统。引入了一个新的依赖于多胞不确定性和耦合信息的李雅普诺夫函数，以减少保守性。建立了保证增广估计误差系统随机稳定性和性能的充分条件。然后，根据这些条件进一步得到了估计器增益。最后，通过一个数值实例验证了结果的有效性。
