Title :
A new k-winners-take-all neural network
Author :
Liu, Shubao ; Wang, Jun
Author_Institution :
Dept. of Autom. & Comput. Eng., Chinese Univ. of Hong Kong, Shatin, China
fDate :
31 July-4 Aug. 2005
Abstract :
In this paper, the k-winners-take-all (KWTA) operation is converted to an equivalent constrained convex quadratic optimization formulation. A simplified dual neural network, called KWTA network, is further developed for solving the convex quadratic programming (QP) problem. The KWTA network is shown to be globally convergent to the exact optimal solution of the QP problem. Simulation results are presented to show the effectiveness and performance of the KWTA network.
Keywords :
convex programming; neural nets; quadratic programming; convex quadratic optimization; convex quadratic programming; k-winners-take-all neural network; Associative memory; Automation; Computer networks; Constraint optimization; Feature extraction; Logic gates; Neural networks; Quadratic programming; Signal processing; Very large scale integration;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1555939