This paper deals with the issue of state estimation for the class of bidirectional associative memory cellular neural networks (BAMCNNs) involving multi-proportional delays. The main objective of this problem is to sketch a state estimator by utilizing the known output measurements of the proposed network in such a way that the dynamics of the estimation error system is globally asymptotically stable. By formulating a proper Lyapunov-Krasovskii functional (LKF) and making use of the Lyapunov stability theory, delay-dependent and independent sufficient conditions are obtained in the form of linear matrix inequalities (LMIs) to achieve the prescribed estimation performance. By using specified parameter values, the state estimator gain matrices are calculated by means of solving the obtained LMIs. Finally, numerical illustrations are explored to show the applicability and advantages of the proposed theoretical results.

中文翻译：

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具有多比例延迟的 BAM 细胞神经网络的延迟相关和独立状态估计

本文涉及涉及多比例延迟的双向关联记忆细胞神经网络 (BAMCNN) 类的状态估计问题。这个问题的主要目标是通过利用所提出的网络的已知输出测量来绘制状态估计器，以使估计误差系统的动态全局渐近稳定。通过制定适当的 Lyapunov-Krasovskii 泛函 (LKF) 并利用 Lyapunov 稳定性理论，以线性矩阵不等式 (LMI) 的形式获得依赖于延迟和独立的充分条件，以实现规定的估计性能。通过使用指定的参数值，通过求解获得的 LMI 来计算状态估计器增益矩阵。最后，