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Neural Networks
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Neural Networks gives professionals and students the tools to train, visualize, and validate neural network models. It supports a comprehensive set of neural network structures--including radial basis function, feedforward, dynamic, Hopfield, perceptron, vector quantization, unsupervised, and Kohonen networks. It implements state-of-the-art training algorithms like Levenberg-Marquardt, Gauss-Newton, and steepest descent. Neural Networks also includes special functions to address typical problems in data analysis, such as function approximation, classification and detection, clustering, nonlinear time series, and nonlinear system identification problems. Neural Networks is equally suited for advanced and inexperienced users. The built-in palettes facilitate the input of any parameter for the analysis, evaluation, and training of your data. The online documentation contains a number of detailed examples that demonstrate different neural network models. You can solve many problems simply by applying the example commands to your own data. Neural Networks also provides numerous options to modify the training algorithms. The default values have been set to give good results for a large variety of problems, allowing you to get started quickly using only a few commands. As you gain experience, you will be able to customize the algorithms to improve the performance, speed, and accuracy of your neural network models. With Neural Networks and Mathematica, you will have access to a robust modeling environment that lets you test and explore neural network models faster and easier than ever before. Auf CD-Rom und als Download erhältlich. |
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