Robust ART-2 neural network learning framework

Abstract

The ART-2 network is a typical adaptive resonance theory based neural network approach for clustering purpose and has been successfully used in many fields. However, one of the fatal shortcomings of traditional ART-2 is that its final results heavily depend on a pre-defined fixed vigilance threshold parameter, which makes it infeasible to be applied in different complicated applications. Another disadvantage of traditional ART-2 method is that the number of categories in the network will increase all the time with the continuous input. Considering these points, an improved algorithm of ART-2 has been presented in this paper called the Robust ART-2. We first systematically analyze the dynamic changes of the optimal vigilance threshold with the succession inputs and propose a new adaptive method to make the network itself can automatically choose the optimal threshold in various situations. Then we introduce a constraint parameter to confine the scale of ART-2 network by limiting the maximal number of categories of network. Simulation experiments including artificial and benchmark data sets demonstrate the effectiveness of our algorithm.