Intermittent State Observer Design for Neural Networks With Reaction–Diffusion Terms Using Partial Measurements
Xiaona Song, Mi Wang, Shuai Song, Choon Ki Ahn
Abstract
This article develops a novel state observer for delayed reaction–diffusion neural networks by utilizing incomplete measurements. To reduce the transmission cost efficiently, the space domain is divided into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> parts and only partial information needs to be measured in every subdomain, such as a point in one-dimensional space, a line and a plane in two- and three-dimensional space, respectively. In addition, the time domain is divided: the measured output signals are transmitted intermittently. Then, new conditions that assure the asymptotic stability of observation error system are derived based on the Lyapunov direct method and several inequality techniques. Finally, the proposed approach’s effectiveness is demonstrated via three numerical examples.