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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 6798905, 12 pages
Research Article

Towards Utilization of Neurofuzzy Systems for Taxonomic Identification Using Psittacines as a Case Study

1Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA
2Department of Computer Science, University of Illinois Springfield, Springfield, IL 62703, USA

Received 19 October 2015; Accepted 29 December 2015

Academic Editor: Baoding Liu

Copyright © 2016 Shahram Rahimi et al. 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.


Demonstration of the neurofuzzy application to the task of psittacine (parrot) taxonomic identification is presented in this paper. In this work, NEFCLASS-J neurofuzzy system is utilized for classification of parrot data for 141 and 183 groupings, using 68 feature points or qualities. The reported results display classification accuracies of above 95%, which is strongly tied to the setting of certain parameters of the neurofuzzy system. Rule base sizes were in the range of 1,750 to 1,950 rules.