Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays. (May 2018) - Yang, Xujun; Li, Chuandong; Huang, Tingwen; Song, Qiankun; Huang, Junjian
Highlights: The main objective of this paper is to investigate the problem of synchronization of delayed FMCVNNs with unknown parameters. To our best knowledge, the parameters of most previous literatures of fractional-order memristor-based complex-valued neural networks (FMCVNNs) are deterministic. Few works have been done on the synchronization of FMCVNNs with parameter uncertainties. Main contributions: Based on the fractional differential equations, complex-valued network theory and differential inclusion theory, the drive-response models of FMCVNNs with delays and parameter uncertainties are established. By employing feedback control strategy and Lyapunov direct method, several sufficient criteria ensuring the global asymptotical synchronization for the concerned network models are derived. Numerical examples are designed to verify the availability and feasibility of the theoretical results. Abstract: 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.