DocumentCode
396696
Title
Synthesis of a k-winners-take-all neural network using linear programming with bounded variables
Author
Ferreira, L.V. ; Kaszkurewicz, E. ; Bhaya, A.
Author_Institution
Dept. of Electr. Eng., Fed. Univ. of Rio de Janeiro, Brazil
Volume
3
fYear
2003
fDate
20-24 July 2003
Firstpage
2360
Abstract
A k-winners-take-all (KWTA) problem is formulated as a linear programming (LP) problem with bounded variables. The solution set of the LP problem determines the winners. The LP problem is converted into an unconstrained optimization problem with two exact penalty functions, that is solved by using a gradient descent method implemented as a neural network. Theoretical results ensuring the convergence to the correct solution are provided.
Keywords
convergence; gradient methods; linear programming; network synthesis; neural nets; optimisation; KWTA; LP; bounded variables; convergence; gradient descent method; k-winners-take-all problem; linear programming; neural network synthesis; penalty functions; unconstrained optimization problem; Circuits; Convergence; Functional programming; Lagrangian functions; Linear programming; Lyapunov method; Network synthesis; Neural networks; Optimization methods; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223781
Filename
1223781
Link To Document