title: Event-triggered H∞ filter design for delayed neural network with quantization.
abstract: This paper is concerned with H∞ filter design for a class of neural network systems with event-triggered communication scheme and quantization. Firstly, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broadcasted and transmitted to quantizer, which can save the limited communication resource. Secondly, a logarithmic quantizer is used to quantify the sampled data, which can reduce the data transmission rate in the network. Thirdly, considering the influence of the constrained network resource, we investigate the problem of H∞ filter design for a class of event-triggered neural network systems with quantization. By using Lyapunov functional and linear matrix inequality (LMI) techniques, some delay-dependent stability conditions for the existence of the desired filter are obtained. Furthermore, the explicit expression is given for the designed filter parameters in terms of LMIs. Finally, a numerical example is given to show the usefulness of the obtained theoretical results.
authors.affiliation: College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China; School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China. Electronic address: liujinliang@vip.163.com.
authors.forename: Jinliang
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China.
authors.forename: Jia
authors.initials: J
authors.lastname: Tang
authors.affiliation: School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China.
authors.forename: Shumin
authors.initials: S
authors.lastname: Fei
mesh_terms: D000465:Algorithms; D003198:Computer Simulation / Q000639:trends; D016571:Neural Networks, Computer; D013997:Time Factors
keywords: Event-triggered scheme; Exponential stability; Neural networks; Quantization; filter