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