Wolfram Research

Neural Networks

Neural Net Models for Teachers and Students

Neural Networks

Artificial neural networks have revolutionized the way researchers solve many complex and real-world problems in image processing, engineering, science, economics and finance. The Wolfram Language includes a wide range of state-of-the-art integrated machine learning capabilities, including a neural network framework, and the Wolfram Neural Net Repository contains neural network models available for training complicated nets on real-world data. This Neural Networks add-on package is intended for teaching and investigating simple neural net models on small datasets.

The Neural Networks package gives teachers and students tools to train, visualize and validate simple 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 training algorithms such as 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.

The Neural Networks package features palettes that facilitate the input parameters for the analysis, evaluation and training of your data. The documentation contains a number of detailed examples that demonstrate different neural network models and algorithms. You can solve many problems simply by applying the example commands to your own data. The Neural Networks package 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.

The package comes with electronic documentation that contains a number of detailed examples that demonstrate the use of the different neural network models, making the Neural Networks package an excellent teaching tool either for independent study or for use in neural network courses.

About the Developer

Jonas Sjöberg is a professor and the head of the mechatronic research group at Chalmers University. Dr. Sjöberg's research involves mechatronic-related fields, such as signal processing and control. Within these fields, he focuses on model-based methods, simulations, system identification and optimization for design and product development of mechatronic systems.

Product Support

The Neural Networks add-on is developed and supported by Dr. Jonas Sjöberg.

Dr. Jonas Sjöberg
email: jonas.sjoberg@chalmers.se

Neural Networks 1.2 requires Mathematica 9 or 10 and is available for all Mathematica platforms.