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Simulating Neural Networks with Mathematica
Simulating Neural Networks with Mathematica
by James A. Freeman
  • Publisher: Addison-Wesley
  • Year: 1994
  • ISBN: 020156629X (Hardcover)
  • 341 pp
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
Computer Science
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