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Theoretical and experimental analysis of the first layer in neural networks for 3D pattern
Neural networks as components 171011
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adaptive analysis antecedent networks applications approach artificial neural networks associative memory backpropagation binary bipolar Butterworth filter centroids classification cluster conclusion networks convergence corresponding data set DDEKF defined detection dilution distributed edge edge detection equation error example feature map feedforward networks filter FiNN fuzzy control fuzzy logic fuzzy sets fuzzy systems Genetic Algorithms GKCN GNAT gradient gradient descent hidden layer hidden units information retrieval initial input vector internal representations iterations keywords Kohonen learning rate linear machine mathematical matrix membership functions method n-gram network architecture network training neural inputs neuron nodes noise nonlinear Omron operation optimal paper parameters pattern recognition performance pixel prediction problem properties prototypes recurrent network recurrent neural networks represented response RMLP robots sample segmentation selection self-organizing sequence SFCM sigmoidal signal simulations Sobel step synaptic target Technology total number training set unsupervised learning updates values weight vector