Delay-Dependent and Independent State Estimation for BAM Cellular Neural Networks with Multi-Proportional Delays

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.

Automated summary

This paper addresses the state estimation problem for bidirectional associative memory cellular neural networks with multi-proportional delays, aiming to achieve global asymptotic stability of the estimation error system. Sufficient conditions in the form of LMIs are obtained by utilizing Lyapunov stability theory, and numerical illustrations are provided to demonstrate the applicability and advantages of the proposed theoretical results.