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An online pruning strategy for supervised ARTMAP-based neural networks

journal contribution
posted on 2009-05-01, 00:00 authored by S Tan, M Rao, Chee Peng LimChee Peng Lim
Identifying an appropriate architecture of an artificial neural network (ANN) for a given task is important because learning and generalisation of an ANN is affected by its structure. In this paper, an online pruning strategy is proposed to participate in the learning process of two constructive networks, i.e. fuzzy ARTMAP (FAM) and fuzzy ARTMAP with dynamic decay adjustment (FAMDDA), and the resulting hybrid networks are called FAM/FAMDDA with temporary nodes (i.e. FAM-T and FAMDDA-T, respectively). FAM-T and FAMDDA-T possess a capability of reducing the network complexity online by removing unrepresentative neurons. The performances of FAM-T and FAMDDA-T are evaluated and compared with those of FAM and FAMDDA using a total of 13 benchmark data sets. To demonstrate the applicability of FAM-T and FAMDDA-T, a real fault detection and diagnosis task in a power plant is tested. The results from both benchmark studies and real-world application show that FAMDDA-T and FAM-T are able to yield satisfactory classification performances, with the advantage of having parsimonious network structures.

History

Journal

Neural computing and applications

Volume

18

Issue

4

Pagination

387 - 395

Publisher

Springer UK

Location

London, England

ISSN

0941-0643

eISSN

1433-3058

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal