An effective sampling scheme for better multi-layer perceptrons
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Investigating better multi-layer perceptrons for the task of classification
WSEAS Transactions on Computers
The effect of training set size for the performance of neural networks of classification
WSEAS Transactions on Computers
Ozone day prediction with radial basis function networks
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II
Using quick decision tree algorithm to find better RBF networks
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Development of the knowledge-based learning system for distance education
International Journal of Intelligent Systems
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Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots. All C# source code is available online for easy downloading.