Self-organizing map with time-invariant learning rate and its exponential stability analysis

Abstract

In this paper a new self-organizing map (SOM) is developed that is suitable for digital hardware implementation. In the proposed neural model, a time-invariant learning rate is used, whereas the original Kohonen SOM uses a time-varying learning rate. There is a binary re-inforcement term in order to compensate for the lowered learning ability due to the constant learning rate. The proposed SOM is exponentially stable. The experimental results conducted with two different types of data show that the proposed method has better learning ability than the original SOM.