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Simulating Neural Networks with Mathematica
Author

James A. Freeman
Organization: Artificial Intelligence Lab, Loral Space Information Systems
Book information

Publisher: Addison-Wesley
Copyright year: 1994
ISBN: 020156629X
Medium: Hardcover
Pages: 341
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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
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.
Subject

*Applied Mathematics > Computer Science
Keywords

neural networks, computer science, genetic algorithms, pattern recognition, Jordan normal form, back propagation, traveling salesman problem, travelling salesman problem
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TravelingSalesperson.m (2.4 KB) - Mathematica package