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Advances in Fuzzy Systems
Volume 2012, Article ID 206121, 8 pages
Research Article

Fuzzy Lattice Reasoning for Pattern Classification Using a New Positive Valuation Function

Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran

Received 20 April 2012; Accepted 23 July 2012

Academic Editor: F. Herrera

Copyright © 2012 Yazdan Jamshidi Khezeli and Hossein Nezamabadi-pour. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks.