Temporal sequence learning and recognition with dynamic SOM

The purpose of the paper is to propose a map-like artificial neural network for temporal sequence pattern clustering. The map construction in our presentation is related to the self-organizing map (SOM) idea. The SOM idea was originally designed for static pattern learning and recognition. It has been found efficient for organizing high dimensional data sets. One of the biggest limitations of the traditional SOM technique is caused by its static characteristics. We propose a new neural network construction model and its corresponding training algorithm based on traditional SOM training technology and backpropagation training technology. It overcomes the static limitation of traditional SOM and tries to reach a new stage for dynamic pattern clustering, and recognition. At the end of the paper, we give some experimental results for testing this proposed method on real speech data.