Department of Industrial Education and Technology, National Changhua University of Education, 500 Changhua, Taiwan, ROC.
Cogn Neurodyn. 2011 Jun;5(2):133-43. doi: 10.1007/s11571-010-9135-8. Epub 2010 Sep 18.
The state estimation problem for discrete-time recurrent neural networks with both interval discrete and infinite-distributed time-varying delays is studied in this paper, where interval discrete time-varying delay is in a given range. The activation functions are assumed to be globally Lipschitz continuous. A delay-dependent condition for the existence of state estimators is proposed based on new bounding techniques. Via solutions to certain linear matrix inequalities, general full-order state estimators are designed that ensure globally asymptotic stability. The significant feature is that no inequality is needed for seeking upper bounds for the inner product between two vectors, which can reduce the conservatism of the criterion by employing the new bounding techniques. Two illustrative examples are given to demonstrate the effectiveness and applicability of the proposed approach.
本文研究了具有区间离散和无穷分布时变时滞的离散时间递归神经网络的状态估计问题，其中区间离散时滞在给定范围内。假设激活函数是全局 Lipschitz 连续的。基于新的界估计技术，提出了一个与时滞相关的状态估计器存在性条件。通过求解某些线性矩阵不等式，设计了一般的全阶状态估计器，以确保全局渐近稳定性。显著的特点是，在寻求两个向量内积的上界时，不需要不等式，这可以通过采用新的界估计技术来减少判据的保守性。给出了两个说明性示例，以验证所提出方法的有效性和适用性。
