Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6862917 | Neural Networks | 2018 | 17 Pages |
Abstract
This paper investigates Hâ state estimation problem for a class of semi-Markovian jumping discrete-time neural networks model with event-triggered scheme and quantization. First, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broad-casted and transmitted to the quantizer, which can save the limited communication resource. Second, a novel communication framework is employed by the logarithmic quantizer that quantifies and reduces the data transmission rate in the network, which apparently improves the communication efficiency of networks. Third, a stabilization criterion is derived based on the sufficient condition which guarantees a prescribed Hâ performance level in the estimation error system in terms of the linear matrix inequalities. Finally, numerical simulations are given to illustrate the correctness of the proposed scheme.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
R. Rakkiyappan, K. Maheswari, G. Velmurugan, Ju H. Park,