Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays Academic Article

abstract

• 2018 Elsevier Ltd This paper talks about the global asymptotical synchronization problem of delayed fractional-order memristor-based complex-valued neural networks with uncertain parameters. Under the framework of Filippov solution and differential inclusion theory, several sufficient criteria ensuring the global asymptotical synchronization for the addressed drive-response models are derived, by means of Lyapunov direct method and comparison theorem. In addition, two numerical examples are designed to verify the correctness and effectiveness of the theoretical results.

authors

• Huang, Tingwen

published proceedings

• CHAOS SOLITONS & FRACTALS

author list (cited authors)

• Yang, X., Li, C., Huang, T., Song, Q., & Huang, J.

citation count

• 67

complete list of authors

• Yang, Xujun||Li, Chuandong||Huang, Tingwen||Song, Qiankun||Huang, Junjian

publication date

• May 2018

publisher

• Elsevier  Publisher

published in

• Chaos, Solitons and Fractals  Journal

keywords

• Complex-valued Neural Networks
• Fractional Order
• Memristor
• Synchronization
• Time Delay
• Uncertain Parameter

Digital Object Identifier (DOI)

• 10.1016/j.chaos.2018.03.016

start page

• 105

end page

• 123

volume

• 110

URL

• http://dx.doi.org/10.1016/j.chaos.2018.03.016