论文信息 - On the reduction of complexity in the architecture of fuzzy ARTMAP with dynamic decay adjustment

On the reduction of complexity in the architecture of fuzzy ARTMAP with dynamic decay adjustment

Abstract This paper presents a hybrid network (FAMDDA-T) comprising the Fuzzy ARTMAP (FAM) neural network and the Dynamic Decay Adjustment (DDA) algorithm with an online pruning strategy. Twelve benchmark datasets are used to demonstrate the effectiveness of FAMDDA-T. The results of FAMDDA-T are compared with those of FAMDDA (without pruning), and the Radial Basis Function Network with DDA (RBFN-DDA) as well as its pruning version (RBFN-DDA-T). It is observed that, when compared with other DDA-based networks, FAMDDA-T is able to form a parsimonious network structure and, at the same time, to maintain a high level of network generalization in tackling pattern classification problems.

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