DocumentCode
1810688
Title
Lotto-type competitive learning and its stability
Author
Luk, Andrew ; Lien, Sandra
Author_Institution
St B&P Neural Investments Pty Ltd., Australia
Volume
2
fYear
1999
fDate
36342
Firstpage
1425
Abstract
This paper introduces a generalised idea of lotto-type competitive learning (LTCL), where one or move winners exist. The winners are divided into tiers, with each tier being rewarded differently. All losers are penalised equally. A set of dynamic LTCL equations is then introduced to assist in the study of the stability of the simplified idea of LTCL. It is shown that if a K-orthant exists in the LTCL state space, which is an attracting invariant set of the network flow, it will converge to a fixed point
Keywords
convergence; neural nets; stability; state-space methods; unsupervised learning; competitive learning; convergence; lotto-type learning; neural nets; stability; state space; Australia; Equations; Investments; Neural networks; Neurons; Prototypes; Stability analysis; State-space methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.831173
Filename
831173
Link To Document