𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Combinatorial optimization by Hopfield networks using adjusting neurons

✍ Scribed by Yao Liang

Book ID
103107450
Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
590 KB
Volume
94
Category
Article
ISSN
0020-0255

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Solving inequality constrained combinato
Solving inequality constrained combinatorial optimization problems by the hopfield neural networks
✍ Shigeo Abe; Junzo Kawakami; Kotaroo Hirasawa πŸ“‚ Article πŸ“… 1992 πŸ› Elsevier Science 🌐 English βš– 543 KB

The Hop.fteld neural networks are ~:~tended to handle inequality constraints where linear combinations of variables are lower-or upper-bounded. Then b)' eigenvahw analysis, the effects q/'the inequality constraints are analyzed and the lbllowing results are obtained" (a) f a combinatorial solution o

Optimal path determination in a graph by
Optimal path determination in a graph by hopfield neural network
✍ S. Cavalieri; A. Di Stefano; O. Mirabella πŸ“‚ Article πŸ“… 1994 πŸ› Elsevier Science 🌐 English βš– 661 KB

Recurrent stable neural networks seems to represent an mteresting alternattve to classical algortthms for the search for optimal paths in a graph In this paper a Hopfield neural network ts adopted to solve the problem of findmg the shortest path between two nodes of a graph. The results obtained poi

Combinatorial optimization by using a ne
Combinatorial optimization by using a neural network operating in block-sequential mode
✍ Fumiyuki Shiratani; Kimiaki Yamamoto πŸ“‚ Article πŸ“… 1994 πŸ› John Wiley and Sons 🌐 English βš– 490 KB

## Abstract In a laterally connected neural network which has continuous output functions and has its state updated in block‐sequential mode at discrete times, a condition inequality is derived for the slope of the output function and the stepwidth of the updating rule which ensures that the networ