Global stabilization of memristor-based fractional-order neural networks with delay via output-feedback control Academic Article

abstract

• In this paper, the memristor-based fractional-order neural networks (MFNN) with delay and with two types of stabilizing control are described in detail. Based on the Lyapunov direct method, the theories of set-value maps, differential inclusions and comparison principle, some sufficient conditions and assumptions for global stabilization of this neural network model are established. Finally, two numerical examples are presented to demonstrate the effectiveness and practicability of the obtained results.

authors

• Huang, Tingwen

published proceedings

• MODERN PHYSICS LETTERS B

author list (cited authors)

• Chen, J., Li, C., Huang, T., & Yang, X.

citation count

• 19

complete list of authors

• Chen, Jiyang||Li, Chuandong||Huang, Tingwen||Yang, Xujun

publication date

• February 2017

publisher

• World Scientific Publishing  Publisher

published in

• Modern Physics Letters B  Journal

keywords

• Delay
• Fractional Order
• Memristor-based Neural Networks
• Stabilization

Digital Object Identifier (DOI)

• 10.1142/S0217984917500312

start page

• 1750031

end page

• 1750031

volume

• 31

issue

• 5

URL

• http://dx.doi.org/10.1142/s0217984917500312