IEEE Trans Neural Netw Learn Syst. 2018 Apr;29(4):1046-1057. doi: 10.1109/TNNLS.2016.2635080. Epub 2017 Feb 6.
This paper deals with the event-based finite-time state estimation problem for a class of discrete-time stochastic neural networks with mixed discrete and distributed time delays. In order to mitigate the burden of data communication, a general component-based event-triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time-point according to a specific triggering condition. A new concept of finite-time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling-like time function. The objective of the addressed problem is to construct an event-based state estimator to estimate the neuron states such that, in the presence of both mixed time delays and external noise disturbances, the dynamics of the estimation error is finite-time bounded in the mean square with a prescribed error upper bound. Sufficient conditions are established, via stochastic analysis techniques, to guarantee the desired estimation performance. By solving an optimization problem with some inequality constraints, the explicit expression of the estimator gain matrix is characterized to minimize the settling-like time. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme.
本文针对一类具有混合离散和分布式时滞的离散时间随机神经网络，研究了基于事件的有限时间状态估计问题。为了减轻数据通信的负担，提出了一种通用的基于组件的事件触发传输机制，根据特定的触发条件来确定在某些时间点是否应将测量输出释放到估计器。通过引入类似于 settling 的时间函数，提出了一种新的均方有限时间有界性概念，以量化估计性能。所解决问题的目标是构建一个基于事件的状态估计器来估计神经元状态，使得在存在混合时滞和外部噪声干扰的情况下，估计误差的动力学在均方意义上是有限时间有界的，具有给定的误差上界。通过随机分析技术，建立了充分条件来保证期望的估计性能。通过求解具有一些不等式约束的优化问题，得到了估计器增益矩阵的显式表达式，以最小化类似于 settling 的时间。最后，通过数值仿真示例验证了所提出的估计器设计方案的有效性。
