Key Features * Neural network design, training, and simulation * Pattern recognition, clustering, and data-fitting tools * Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrent * Unsupervised networks including self-organizing maps and competitive layers * Preprocessing and postprocessing for improving the efficiency of network training and assessing network performance * Modular network representation for managing and visualizing networks of arbitrary size * Routines for improving generalization to prevent overfitting * Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications

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