6.3 Synaptic convergence to centroids:AVQ Algorithms Competitive AVQ Algorithms 1.Initialize synaptic vectors:m(0)=x(i),i=1,......,m 2.For random sample x(t),find the closest (winning)synaptic vector m (t):m,(t)-x(t)=minlm,(t)-x(t where+gives the squared Euclidean norm of x 3.Update the wining synaptic vectors m()by the UCL,SCL,or DCL learning algorithm. 2003.11.19 102003.11.19 10 6.3 Synaptic convergence to centroids: AVQ Algorithms Competitive AVQ Algorithms 1. Initialize synaptic vectors: mi (0) = x(i) , i =1,......,m 2.For random sample , find the closest (“winning”) synaptic vector : x(t) m (t) j ( ) ( ) min ( ) ( ) j i i m t x t m t x t − = − 3.Update the wining synaptic vectors by the UCL ,SCL,or DCL learning algorithm. m (t) j 2 2 2 1 ....... where x x x = + + n gives the squared Euclidean norm of x