An improved ART2 neural network: Resisting pattern drifting through generalized similarity and confidence measures

ART2 network is a kind of non-supervised neural network based on the adaptive resonance theory, and has been widely used in real-time classification because of its rapid response and real-time learning. There are two problems in the traditional ART2 network: the indistinguishable of the different samples with similar phase and the pattern drifting caused by the gradual changing data. In this paper, we propose an improvement version of the ART2 network based on the generalized similarity and confidence measures, named GSC-ART2 (Generalized Similarity Confidence ART2) network. In this neural network, the similarity detection mechanism based on the generalized similarity measure is proposed to solve the indistinguishable problem of the different samples with similar phase. Furthermore, the updating method of the connection weights considering both the generalized similarity and the confidence measures is proposed to inhibit pattern drifting problem. The stimulation data is created to evaluate the proposed GSC-ART2 network, and the outcomes approved that the performance of the GSC-ART2 network is better than traditional ART2 network about the classification and the inhibiting pattern drifting. The GSC-ART2 network would become a universal solution to the pattern drifting problem in various applications.