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Techniques for prediction in spatial maps can be based on associative neural network models. Unfortunately, the performance of standard associative memories depends on the number of training patterns stored in the memory; moreover it is very sensitive to mutual correlations of the stored patterns. In order to overcome limitations imposed by processing of a large number of mutually correlated spatial patterns, we have designed the Hierarchical Associative Memory model which consists of arbitrary number of associative memories hierarchically grouped into several layers. In order to further improve its recall abilities, we have proposed new modification of our model. In this paper, we also present experimental results focused on recall ability of designed model and their analysis by means of mathematical statistics.

Hierarchical Associative Memories: The Neural Network for Prediction in Spatial Maps