An adaptive growing grid model for a non-stationary environment

Chihli Hung, Stefan Wermter, Yu Liang Chi, Chih Fong Tsai

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)


The self-organizing map (SOM) represents high-dimensional input samples by a 2-dimensional output topological structure, whereby similar input samples are mapped onto the same output unit or neighboring units on a map for visualization. Although many extended SOM models have been proposed, the need to determine a grid structure of SOM before learning, and the lack of adaptability to rapid changes in input data have not yet been fully overcome. This research proposes an adaptive growing grid (AGG) model, which is a novel neural self-organizing map (SOM), for projecting high-dimensional input samples onto an output grid. Due to the need for a grid structure for visualization, the AGG uses both growing and pruning functions and an adaptive learning process in order to adapt its output grid structure and learning function to constantly and rapidly changing input data in a non-stationary environment. The proposed AGG is tested on four basic data sets and one cross-domain data set. In addition, the t-test is used to test whether the proposed AGG outperforms the benchmark model, the growing grid (GG). Based on three evaluation measures, i.e. average quantization error (AQE), topographic error (TE) and dead unit ratio (DUR), the AGG significantly outperforms the GG in a non-stationary environment.

出版狀態已出版 - 14 9月 2023


深入研究「An adaptive growing grid model for a non-stationary environment」主題。共同形成了獨特的指紋。