摘要
ART-2是一种基于自适应谐振理论的自组织神经网络,广泛应用于模式聚类与识别等方面.本文介绍原始的 ART-2的结构和运算过程,分析它的训练算法,探讨其固有局限性.归纳总结各主要改进 ART-2的背景、目标和实现,评述它们的特征及适应场合.最后指出进一步改进 ART-2的一些思路,在解决具体问题运用各方法的一些参考原则和 ART-2的理论应用价值.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第5期667-674,共8页
Pattern Recognition and Artificial Intelligence
基金
国家星火计划项目(No.2003EA770007)
湖南省自然科学基金项目(No.00JJY2061)资助
关键词
自适应谐振理论(ART)
模式聚类与识别
相似度
匹配度
相位信息
幅值
重置
Adaptive Resonance Theory （ART）, Pattern Clustering and Recognition,Similarity, Match, Phase Information, Amplitude, Reset
