Simulating Neural Networks With Mathematica Download Usc

Posted on by
Simulating Neural Networks With Mathematica Download Usc Rating: 8,0/10 774reviews

Title Neural Networks Author Organization: Wolfram Research, Inc. Address: 100 Trade Center Dr. Champaign, IL 61820 Phone: (217) 398-0700 Fax: (217) 398-0747 Email: URL: Description Neural Networks is an innovative Mathematica application package designed to train, visualize, and validate neural network models. Hanvon Touchpad B10 Drivers. It combines Mathematica's powerful number-crunching and visualization capabilities with a comprehensive set of neural network structures and training algorithms. The result is an incredibly interactive and flexible environment for training and simulating artificial neural networks. Subject >>>Keywords neural networks, data fitting, data, analysis, software, modeling, nonlinear, models, neural network structure, radial basis function, RBF, feedforward network, dynamic network, Hopfield network, perceptron, network, vector quantization, VQ, unsupervised networks, Kohonen, training algorithms, training functions, Levenberg-Marquardt, Gauss-Newton, steepest descent, backpropagation, function approximation, classification, prediction, dynamic systems modeling, time series, auto-associative memory, clustering, self-organizing maps URL.

Simulating Neural Networks With Mathematica Download Usc

Note: To run this Demonstration you need Mathematica 7+ or the free Mathematica Player 7EX. Download or upgrade to Mathematica Player 7EX. Deltora Quest Cavern Fear Pdf To Excel.

Description Introduces the operations and application of neural networks in the context of Mathematica's programming language. Shows professionals and students how to use Mathematica to simulate neural network operations and to assess neural network behavior and performance. The electronic supplement provides the source code for the programs in the book. Contents Introduction to Neural Networks and Mathematica Training by Error Minimization Backpropagation and Its Variants Optimization and Constraint Satisfaction Feedback and Recurrent Networks Adaptive Resonance Theory Genetic Algorithms Related Topics.