6.3 Synaptic convergence to centroids: AvQ algorithms Competitive learning adaptively quantizes the input pattern space Rn. Probability density function p(x)characterizes the continuous distributions of patterns in r We shall prove that competitive AvQ synaptic vector m converge exponentially quickly to pattern-class centroids and more generally, at equilibrium they vibrate about the centroids in a brownian motion 2003.11.192003.11.19 6 6.3 Synaptic convergence to centroids: AVQ Algorithms We shall prove that competitive AVQ synaptic vector converge exponentially quickly to pattern-class centroids and, more generally, at equilibrium they vibrate about the centroids in a Browmian motion. m j Competitive learning adaptively quantizes the input pattern space . Probability density function characterizes the continuous distributions of patterns in . n R p(x) n R