Neural networks for classification of 2-D patterns

The paper presents the application of three different types of neural networks to the 2D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier transform of the data describing the shape of the pattern. Application of different neural network structure results in different accuracy of recognition and classification. The numerical experiments performed for the recognition of the shapes of airplanes have shown the superiority of the hybrid structure