Abstract
To increase the convergence speed for tackling the complex-valued system of linear equation (CVSLE) problem online, a novel fully complex-valued gradient neural network (FCGNN) model is proposed. Firstly, to tackle the CVSLE problem in the complex number field instead of the real number field, the negative gradient descent information and a new nonlinear activation function are used to design a FCGNN model. Secondly, the proof for the global convergence property of the FCGNN model is deduced from the strict derivation. Finally, the simulation results show that this FCGNN model has a higher convergence rate than the original gradient neural network (GNN) model and the improved Zhang neural network (IZNN) model.
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.
