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| Survey of ART-2 and Its Improvement |
| ZHOU Xin-Ran1,2, TENG Zhao-Sheng1, LIU Xiao-Bo3 |
1.College of Electrical and Information Engineering, Hunan University, Changsha 410082
2.School of Information Science and Engineering, Central South University, Changsha 410075
3.ChangSha Branch Office of China XINDA Asset Management Corporation, Changsha 410001 |
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Abstract ART-2 based on adaptive resonance theory is a kind of self-organizing neural network and usually utilized in pattern clustering and recognition, etc. In order to satisfy some specific requirements of certain applications or to simplify the hardware implementation,some improved versions of ART-2 have been put forward in recent years. In this paper, the original ART-2 is briefly introduced, its training algorithm is firstly analyzed, and its inherent limitations are explored. The background, objects and implementation of typical improved versions are summarized and generalized, and their properties and suitabilities are remarked on. Finally, the theoretical value and some rules pointing to future application and improvement of ART-2 are shown.
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Received: 12 January 2007
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2007, Vol. 20 
