Adding Learned Expectation Into the Learning Procedure of Self-Organizing Maps

The self-organizing topological map is generalized by adding a learned expectation to its learning procedure, in order to improve its stability in nonstationary environments with unexpected inputs and abnormal noise. Such a learned expectation is realized through a compatibility test which checks whether an input is compatible with the earlier learned patterns on a map unit before the unit starts to adapt to the input. The generalized map consists of multi-maps with a pipeline architecture, equipped with a parallel search strategy which allows for the implementation of the learned expectation to be fulfilled without extra computing costs. Computer experiments on several examples are given to show the characteristics of the generalized map in comparison with the original map.