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-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