Abstract
In this paper, we propose a novel chaotic Hopfield neural network (CHNN), which introduces chaotic noise to each neuron of a discrete-time Hopfield neural network (HNN), and the noise is gradually reduced to zero. The proposed CHNN has richer and more complex dynamics than HNN, and the transient chaos enables the network to escape from local energy minima and to settle down at the global optimal solution. We have applied this method to solve a few traveling salesman problems, and simulations show that the proposed CHNN can converge to the global or near global optimal solutions more efficiently than the HNN.
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.
