Flexible array self-organizing map neural network and its application in complex pattern recognition

Abstract

Due to the fixed array of competing-layer structure, observations distribution features cannot be well reflected by conventional SOM in two-dimensional plane. Aimed at solving this problem, a novel flexible array SOM algorithm (faSOM) is proposed in this paper. This algorithm can adaptively adjust the positions of competing-layer neurons to keep consistent with position of observations. As a result, the neurons in mapping space can maintain the original observation’ features. The faSOM algorithm is successfully applied in pattern recognition of two artificial datasets and red-spotted stonecrop samples. Both theory analysis and experimental results indicate that faSOM is an effective algorithm which can map observation’s inherent feature quickly and accurately. Compared with conventional SOM algorithm, feature mapping effect of faSOM algorithm is much better, because it resolves a typical problem in the conventional SOM that the structure of mapped dataset in competing-layer is distorted.