title: Training winner-take-all simultaneous recurrent neural networks.
abstract: The winner-take-all (WTA) network is useful in database management, very large scale integration (VLSI) design, and digital processing. The synthesis procedure of WTA on single-layer fully connected architecture with sigmoid transfer function is still not fully explored. We discuss the use of simultaneous recurrent networks (SRNs) trained by Kalman filter algorithms for the task of finding the maximum among N numbers. The simulation demonstrates the effectiveness of our training approach under conditions of a shared-weight SRN architecture. A more general SRN also succeeds in solving a real classification application on car engine data.
authors.affiliation: Manuscript received September 26, 2005; revised June 25, 2006 and American Power Conversion Corporation, O'Fallon, MO 63368, USA xindi.cai@apc.com
authors.forename: Xindi
authors.initials: X
authors.lastname: Cai
authors.forename: Danil V
authors.initials: DV
authors.lastname: Prokhorov
authors.forename: Donald C
authors.initials: DC
authors.lastname: Wunsch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D005716:Game Theory; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer