Fuzzy ART and Fuzzy ARTMAP with adaptively weighted distances

Dimitrios Charalampidis; Georgios C. Anagnostopoulos; Michael Georgiopoulos; Takis Kasparis

doi:10.1117/12.458723

11 March 2002 Fuzzy ART and Fuzzy ARTMAP with adaptively weighted distances

Dimitrios Charalampidis, Georgios C. Anagnostopoulos, Michael Georgiopoulos, Takis Kasparis

Proceedings Volume 4739, Applications and Science of Computational Intelligence V; (2002) https://doi.org/10.1117/12.458723
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In this paper, we introduce a modification of the Fuzzy ARTMAP (FAM) neural network, namely, the Fuzzy ARTMAP with adaptively weighted distances (FAMawd) neural network. In FAMawd we substitute the regular L₁-norm with a weighted L₁-norm to measure the distances between categories and input patterns. The distance-related weights are a function of a category's shape and allow for bias in the direction of a category's expansion during learning. Moreover, the modification to the distance measurement is proposed in order to study the capability of FAMawd in achieving more compact knowledge representation than FAM, while simultaneously maintaining good classification performance. For a special parameter setting FAMawd simplifies to the original FAM, thus, making FAMawd a generalization of the FAM architecture. We also present an experimental comparison between FAMawd and FAM on two benchmark classification problems in terms of generalization performance and utilization of categories. Our obtained results illustrate FAMawd's potential to exhibit low memory utilization, while maintaining classification performance comparable to FAM.

Citation Download Citation

Dimitrios Charalampidis, Georgios C. Anagnostopoulos, Michael Georgiopoulos, and Takis Kasparis "Fuzzy ART and Fuzzy ARTMAP with adaptively weighted distances", Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); https://doi.org/10.1117/12.458723

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