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Simulating Neural Networks with Mathematica
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| Organization: | Artificial Intelligence Lab, Loral Space Information Systems |
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| Publisher: | Addison-Wesley |
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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
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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.
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neural networks, computer science, genetic algorithms, pattern recognition, Jordan normal form, back propagation, traveling salesman problem, travelling salesman problem
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| Adaline.m (3.4 KB) - Mathematica package | | Art.m (6.7 KB) - Mathematica package | | Backpropagation.m (11.9 KB) - Mathematica package | | Bam.m (1.4 KB) - Mathematica package | | Elman.m (7.4 KB) - Mathematica package | | FunctionalLink.m (2.1 KB) - Mathematica package | | GeneticAlgorithms.m (10.8 KB) - Mathematica package | | Jordan.m (13.8 KB) - Mathematica package | | ProbabilisticNets.m (3.6 KB) - Mathematica package | | TravelingSalesperson.m (2.4 KB) - Mathematica package |
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