SuperNN  1.0.0
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 123]
 NSuperNN
 CActFuncActivation function dispatcher
 CBatchBatch backpropagation
 CBoundsData scaling information, for all input and output neurons
 CConnectionSynaptic connection between two neurons
 CDataData used in training, validation and testing
 CElliotElliot sigmoid-like function
 CElliotSymmetricElliot sigmoid-like function (Symmetric)
 CExceptionThe exception can be identified by the type() method
 CGaussianGaussian function
 CGaussianSymmetricGaussian symmetric function
 CImplBackpropBase class for the standard backpropagation algorithm
 CIncrementalIncremental backpropagation
 CIRpropImproved resilient backpropagation algorithm
 CIRpropL1Modified improved resilient backpropagation algorithm
 CLayerArray of neurons
 CLinearLinear function
 CNBNNeuron by Neuron algorithm
 CHist
 CNetworkArtificial neural network structure that supports arbitrary feedforward topologies, like multilayer perceptrons and fully connected cascade networks
 CNeuronNeuron, that can contain connections to neurons in the next layers
 CRunnerAuxiliary class to ease the usage of an already trained neural network
 CSigmoidActivation functions were not implemented in an OO way due to performance
 CSigmoidSymmetricSigmoid symmetric function
 CSignSign function (net >= 0 ? 1 : -1)
 CSInfoMinimum / maximum scaling information
 CTrainingAlgorithmAbstract class that provides the calculation of the error derivatives and the error accumulation, used by the derived backpropagation training algorithms