一种抗漂移的改进ART2网络GSC-ART2研究

An Improved ART2 Neural Network: Resisting Pattern Drifting through General Similarity and Confidence Measures

ART2是一种基于自适应谐振理论的无监督神经网络,由于其快速响应、实时学习等特点,被广泛地应用在实时聚类问题中。传统的ART2存在幅值信息丢失、容易产生模式漂移的问题,本文针对此不足提出了一种基于广义相似度和置信度的GSCART2网络。其通过引入广义相似度检测和竞争机制,解决了幅值信息丢失的问题。置信度结合广义相似度的权值调整方式抑制了模式漂移并使网络的连接权值更加准确。通过实验表明,GSC-ART2网络在处理幅值相关、样本渐变分类问题上的识别性能均优于传统ART2网络,从而证明了此GSC-ART2网络的有效性,也为解决模式识别中普遍存在的模式漂移问题找到了一种优良的解决方法。

ART2 is a kind of non - supervised neural network based on the Adaptive Resonance Theory, and due to such advantages as rapid response and real - time learning abilities, ART2 has been widely used in real - time clustering prob- lems. In traditional ART2 models, the amplitude information is usually ignored and the problem of pattern drifting often oc- curred. To solve such problems, general similarity and confidence measures are introduced into ART2 to form an improved model - GSC - ART2. Using a vigilance - testing and a competition mechanism based on the general similarity, the prob- lem of amplitude information losing is solved in GSC - ART2. The weights adjustment is modified to consider both the gen- eral similarity and the confidence measures. In such a way, the designed GSC - ART2 inhibits pattern drifting and obtains more accurate network connections. Experiments show that the GSC - ART2 performs better than traditional ART2 in cases where the data possess magnitude information and the data grading or pattern drifting exists. The proposed GSC - ART2 network would become an universal solution to the pattern drifting problem in various applications.