Reachable Set Estimation for a Class of Memristor-Based Neural Networks With Time-Varying Delays | Zendy

Jiemei Zhao | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Reachable Set Estimation for a Class of Memristor-Based Neural Networks With Time-Varying Delays

Author(s) -

Jiemei Zhao

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2777008

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper investigates the reachable set estimation problem for a class of memristor-based neural networks with time-varying delays and bounded disturbances. By constructing a Lyapunov- Krasovskii functional, a sufficient condition for the solvability of the addressed problem is established based on linear matrix inequality. This condition ensuring the existence of an ellipsoid that contains all the states under initial conditions. A stability criterion of memristor-based neural networks with timevarying delays is also given. Two numerical examples are provided to show the effectiveness of the proposed methods.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research