Self-organization and solution of shortest-path optimization problems with memristive networks
Emerging Technologies
2016-06-24 v1 Disordered Systems and Neural Networks
Computational Physics
Abstract
We show that memristive networks-namely networks of resistors with memory-can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time non-locality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.
Keywords
Cite
@article{arxiv.1304.1675,
title = {Self-organization and solution of shortest-path optimization problems with memristive networks},
author = {Yuriy V. Pershin and Massimiliano Di Ventra},
journal= {arXiv preprint arXiv:1304.1675},
year = {2016}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1211.4487