This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network.
- multilayer perceptron neural network
- linear, sigmoid and bipolar sigmoid activation functions
- training, generalization and validation datasets
- backpropagation learning algorithm
- save the trained neural network to a file using binary serialization
- load neural network from file using binary deserialization
- approximation of functions of several variables
- time series prediction
- pattern recognition
- usefulness to solve most of the problems
- simple expert systems
- included simulation program for .NET
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