Abstract
In this paper, a generalized updating rule (GUR) for the modified Hopfield neural network (MHNN) is presented for solving quadratic optimization problems. It is proved that in any sequence of updating modes, the GUR monotonously converges to a fixed point. The upper bound on the gradient of the energy function at the fixed point is given. All ordinary MHNN algorithms are instances of the GUR. It is shown that the class of wide sense sequential updating modes can guarantee the network to achieve local minimums of the energy function.
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.
