Abstract
In this article, the quasi-projective synchronization (QPS) issues about discrete-time fractional-order complex-valued neural networks (DFOCVNNs) are discussed. To realize QPS, the delay-feedback controllers are designed. The main advantage of this controller is that it can search for control gain parameters over a wider range. Applying inequality techniques, Lyapunov method and some lemmas related to discrete fractional calculus, some criteria for QPS are inferred. Meanwhile, the error bound is effectively estimated. Eventually, we display two examples to verify the obtained criteria.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
