Letters: Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

Authors:
Bumhwi Kim;Sang-Woo Ban;Minho Lee
Affiliations:
School of Electrical Engineering & Computer Science, Kyungpook National University, Taegu, Republic of Korea;The Department of Information & Communication Engineering, Dongguk University, Gyeongju, Gyeongbuk, Republic of Korea;School of Electrical Engineering & Computer Science, Kyungpook National University, Taegu, Republic of Korea
Venue:
Neurocomputing
Year:
2011

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Abstract

A new incrementally growing neural network model, called the growing fuzzy topology ART (GFTART) model, is proposed based on integrating the conventional fuzzy ART model with the incremental topology-preserving mechanism of the growing cell structure (GCS) model. This is in addition, to a new training algorithm, called the push-pull learning algorithm. The proposed GFTART model has two purposes: First, to reduce the proliferation of incrementally generated nodes in the F2 layer by the conventional fuzzy ART model based on replacing each F2 node with a GCS. Second, to enhance the class-dependent clustering representation ability of the GCS model by including the categorization property of the conventional fuzzy ART model. In addition, the proposed push-pull training algorithm enhances the cluster discriminating property and partially improves the forgetting problem of the training algorithm in the GCS model.