Time-delayed self-organizing maps

Three related possibilities for representing the sequential aspect of data using the self-organizing map model are studied. The quantitative results of experiments with artificial test data are described, and the most promising solutions for future work are discussed. In the first model, the backwards exponentially averaged input vectors are used as the pattern vector. In the second model, a concatenation model where actual input patterns from previous time slots are concatenated together to form a long pattern vector is used. Then the history is explicitly shown in the input vector. In the third model, an averaging scheme is again used, but with one map to get a first-order representation of the input data. The averaged responses from the first map are used as input patterns for the second map. Thus, the third model consists of a hierarchical structure of maps. It is concluded that the third system is the most interesting because of its accuracy, high tolerance to increasing noise, and high tolerance to the variation of the weighting parameters of the systems