Application of Kohonen's self-organizing feature maps into the problem of selecting the buttons

We applied Kohonen's self-organizing feature maps (SOM) into the problem of selecting the buttons. 3D data spaces are represented by two maps of 2D SOM which are formed on the planes that are projected from 3D objects. Assuming the ability of SOM to approximate the shape of input-data spaces, we select the best matching color-series when reference-data point exists in the SOM. Using this method, very good recognition (96%) was obtained. This method is better than the neural network with backpropagation learning on the points that adjustments of parameters are easy and paradoxical data-relations are permitted.