Dynamic competitive learning for centroid estimation

Presents an analog version of an artificial neural network, termed a differentiator, based on a variation of the competitive learning method. The network is trained in an unsupervised fashion, and it can be used for estimating the centroids of clusters of patterns. A dynamic competition is held among the competing neurons in adaptation to the input patterns with the aid of a novel type of neuron called control neuron. The output of the control neurons provides feedback reinforcement signals to modify the weight vectors during training. The training algorithm is different from conventional competitive learning methods in the sense that all the weight vectors are modified at each step of training. Computer simulation results are presented which demonstrate the behavior of the differentiator in estimating the class centroids. The results indicate the high power of dynamic competitive learning as well as the fast convergence rates of the weight vectors