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Mathematical Problems in Engineering
Volume 2014, Article ID 524304, 7 pages
http://dx.doi.org/10.1155/2014/524304
Research Article

Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier

1School of Resource and Safety Engineering, Central South University, Changsha 410083, China
2Commercial College, Hunan International Economics University, Changsha 410205, China

Received 27 June 2014; Accepted 7 October 2014; Published 21 October 2014

Academic Editor: Peng-Yeng Yin

Copyright © 2014 Hongyan Zuo 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.

Abstract

To overcome the drawback that fuzzy classifier was sensitive to noises and outliers, Mamdani fuzzy classifier based on improved chaos immune algorithm was developed, in which bilateral Gaussian membership function parameters were set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database was used for simulation experiment, classification, and recognition of acoustic emission signals and interference signals from stope wall rock of underground metal mines. The results showed that Mamdani fuzzy classifier based on improved chaos immune algorithm could effectively improve the prediction accuracy of classification of data sets with noises and outliers and the classification accuracy of acoustic emission signal and interference signal from stope wall rock of underground metal mines was 90.00%. It was obvious that the improved chaos immune Mamdani fuzzy (ICIMF) classifier was useful for accurate diagnosis of acoustic emission signal and interference signal from stope wall rock of underground metal mines.