Improved results on state estimation for neural networks with time-varying delays  

作　　者：Tao LI 1 , Shumin FEI 2 , Hong LU 2 (1.School of Instrument Science & Engineering, Southeast University, Nanjing Jiangsu 210096, China 2.Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing Jiangsu 210096, China) 

出　　处：《控制理论与应用（英文版）》2010年第2期215-221,共7页

基　　金：supported by the National Natural Science Foundation of China (No.60764001, 60835001, 60875035)

摘　　要：In this paper, some improved results on the state estimation problem for recurrent neural networks with both time-varying and distributed time-varying delays are presented. Through available output measurements, an improved delay-dependent criterion is established to estimate the neuron states such that the dynamics of the estimation error is globally exponentially stable, and the derivative of time-delay being less than 1 is removed, which generalize the existent methods. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed results.In this paper, some improved results on the state estimation problem for recurrent neural networks with both time-varying and distributed time-varying delays are presented. Through available output measurements, an improved delay-dependent criterion is established to estimate the neuron states such that the dynamics of the estimation error is globally exponentially stable, and the derivative of time-delay being less than 1 is removed, which generalize the existent methods. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed results.

关 键 词：Exponential state estimator Recurrent neural networks Exponential stability Time-varying delays Linear matrix inequality (LMI) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]