document: Self-organizing feature maps predicting sea levels In this paper , a new method for predicting sea levels employing self-organizing feature maps is introduced . For that purpose the maps are transformed from an unsupervised learning procedure to a supervised one . Two concepts , originally developed to solve the problems of convergence of other network types , are proposed to be applied to Kohonen networks : a functional relationship between the number of neurons and the number of learning examples and a criterion to break off learning . The latter one can be shown to be conform with the process of self-organization by using U-matrices for visualization of the learning procedure . The predictions made using these neural models are compared for accuracy with observations and with the prognoses prepared using six models : two hydrodynamic models , a statistical model , a nearest neighbor model , the persistence model , and the verbal forecasts that are broadcast and kept on record by the Sea Level Forecast Service of the Federal Maritime and Hydrography Agency -LRB- BSH -RRB- in Hamburg . Before training the maps , the meteorological and oceanographic situation has to be condensed as well as possible , and the weight and learning vectors have to be made as small as possible . The self-organizing feature maps predict sea levels better than all six models of comparison
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