Geometric Learning Algorithm for Elementary Perceptron and Its Convergence Conditions

摘要

In this paper, the geometric learning algorithm (GLA) is proposed for an elementary perceptron which includes a single output neuron. The GLA is a modified version of the affine projection algorithm (APA) for adap- tive filters. The weight update vector is determined geomet- rically with respect to the orthogonal complement of the k patterns to be classified, where k is the order to the GLA. In the case of the APA, the target of the coefficient update Is a single point which corresponds to the best identification Of the unknown system.