Abstract
In this paper, we analyse mathematically the relationship between the mean field theory network (MFT) model and the continuous-time Hopfield neural network by the use of the theory of dynamical systems. This MFT model, which is obtained by applying the mean field approximation to the Boltzmann machine, is a discrete-time recurrent neural network. We prove that the set of asymptotically stable fixed points of the asynchronous MFT model coincides with the set of asymptotically stable equilibria of the continuous-time Hopfield neural network. Therefore, it is shown that the asynchronous MFT model is equivalent to the Hopfield neural network on the nature of the fixed points (or equilibria). Copyright © 1996 Elsevier Science Ltd.
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.
