NeuralNetwork

Assembly: NN;

Description

The NeuralNetwork class is used to work with artificial neural networks.

Comments

Using this class the user can create, learn and use the back-propagation network or self-organizing Kohonen map.

Class object methods inherited from INeuralNetwork

  Method name Brief description
ApplyConvexCombinationFactor The ApplyConvexCombinationFactor method applies convex combinatorial transformation to the input elements of the network.
CreateNetwork The CreateNetwork method creates a neural network according to the assigned string view.
CreateNetworkEx The CreateNetworkEx method creates a neural network according to the assigned parameters.
DeleteNetwork The DeleteNetwork method deletes neural network.
DeltasMinimumReachedBP The DeltasMinimumReachedBP method returns whether the delta value has attained the assigned level.
ExportSynapses The ExportSynapses method returns string view of the network.
GetClosestNeuron The GetClosestNeuron method returns neuron index, which weight vector being least of all different from the tested input vector.
GetError The GetError method returns code of the last error of the neural network.
GetInputValues The GetInputValues method returns real array of the network input values.
GetLearnRadius The GetLearnRadius method returns value of the learning step for the specified layer.
GetLearnRate The GetLearnRate method returns learning rate value for the specified layer.
GetMaximumWeightDelta The GetMaximumWeightDelta method returns the maximum delta value of the weight of all the synapses.
GetNumberOfInputs The GetNumberOfInputs method returns the number of network inputs.
GetNumberOfLayers The GetNumberOfLayers method returns the number of neural network layers.
GetNumberOfOutputs The GetNumberOfOutputs method returns the number of network outputs.
GetOutputValues The GetOutputValues method returns the array of network output values.
GetOutputWidth The GetOutputWidth method returns the number of the lines in the output layer of the Kohonen self-organizing map.
GetRowWidth The GetRowWidth method returns the number of the lines in the output layer of the Kohonen self-organizing map.
GetRowWidthEx The GetRowWidthEx method returns the number of the lines in the specified layer of the Kohonen self-organizing map.
GetSynapse The GetSynapse method returns the weight value of the specified synapse.
GetUseVectorScalar The GetUseVectorScalar method returns whether the algorithm of scalar multiplication of vectors is used to calculate the distance between neurons in the specified layer.
ImportSynapses The ImportSynapses method loads values of synapse weights from the string view.
InitSynapses The InitSynapses method assigns values of synapse weights for the specified layer according to the assigned parameters.
InitSynapsesConvex The InitSynapsesConvex method assigns values of synapse weights by using the convex combination algorithm.
InitSynapsesConvexEx The InitSynapsesConvexEx method assigns values of synapse weights for the specified layer by using the convex combination algorithm.
LearnBack The LearnBack method performs iteration on learning back-propagation network.
LearnSOFM The LearnSOFM method learns a Kohonen self-organizing map.
NormalizeInputValues The NormalizeInputValues method normalizes network input data.
NormalizeInputValuesEx The NormalizeInputValuesEx method normalizes input values of the specified layer.
NormalizeSynapses The NormalizeSynapses method normalizes values of synapse weights of all the network layers.
NormalizeSynapsesEx The NormalizeSynapsesEx method normalizes values of synapse weights of the specified layer.
PropagateBP The PropagateBP method propagates the signal in the back-propagation network.
PropagateSOFM The PropagateSOFM method propagates the signal on the Kohonen self-organizing network.
SetInputValues The SetInputValues method sets network input values.
SetInputValuesConvex The SetInputValuesConvex method sets network input values by using the convex combinatorial transformation.
SetInputValuesConvexEx The SetInputValuesConvexEx method sets network input values by using the convex combinatorial transformation and ability of normalization.
SetLearnRadius The SetLearnRadius method sets network learning step.
SetLearnRadiusEx The SetLearnRadiusEx method sets network learning step.
SetLearnRate The SetLearnRate method sets learning rate.
SetLearnRateEx The SetLearnRateEx method sets leaning rate for the specified layer.
SetMju The SetMju method sets coefficient of inertia for learning of all the layers of the back-propagation network.
SetMjuEx The SetMjuEx method sets coefficient of inertia of learning for the specified layer of the back-propagation network.
SetNju The SetNju method sets learning speed of the back-propagation network.
SetNjuEx The SetNjuEx method sets learning speed for the specified layer of the back-propagation network.
SetOutputWidth The SetOutputWidth method sets the number of the lines in the output layer of the Kohonen self-organizing map.
SetRowWidth The SetRowWidth method sets the number of the lines in the output layer of the Kohonen self-organizing map.
SetRowWidthEx The SetRowWidthEx method sets the number of the lines in the specified layer of the Kohonen self-organizing map.
SetSigmoidAlpha The SetSigmoidAlpha method sets value of the Alpha coefficient for the sigmoid functions of signal propagation in the network.
SetSigmoidFuncs The SetSigmoidFuncs method sets the type of signal propagation in the network.
SetSigmoidFuncsEx The SetSigmoidFuncs method sets the type of signal propagation in the network for the specified layer.
SetSynapse The SetSynapse method sets the value of the specified synapse weight.
SetUseVectorScalar The SetUseVectorScalar method determines whether to apply the algorithm of scalar multiplication of vectors to calculate the distance between neurons.
SetUseVectorScalarEx The SetUseVectorScalarEx method determines whether to apply the algorithm of scalar multiplication of vectors to calculate the distance between neurons of the specified layer.

See also:

NN Assembly Classes