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Node activation or output based on the weighted
input to the Node.Node as represented
by a series of Node-WeightedConnection pairs.WeightedConnection
based on the backpropagation algorithm.
setWeight
and setLastWeightChange on the
WeightedConnection object.
Neuron output relative to an actual value (useful
for neurons in an output layer).
Neuron output relative to an actual value (useful
for neurons in an output layer).
Neuron output relative to an actual value (useful
for neurons in an output layer).
Neuron output based on errors observed in the outputs
of Neurons which rely on the output of this Neuron.
Neuron output based on errors observed in the outputs
of Neurons which rely on the output of this Neuron.
Neuron output based on errors observed in the outputs
of Neurons which rely on the output of this Neuron.
Neuron output, either relative
to an actual value, or based on error in the outputs of Neurons to which the Neuron feeds.WeightedConnections which are the input connections
to this neuron.
WeightedConnections which are the input connections
to this node.
WeightedConnections which are the output connections
to this neuron.
WeightedConnections which are the output connections
to this node.
WeightedConnections
to a Node
WeightedConnections
to a Node
Node activation or output
based on the hyperbolic tangent function, and of the error in output for the
backpropagation training method using this function.Nodes weighted according to weights contained in associated
WeightedConnections.Node activation or output
based on the logistic function, and of the error in output for the
backpropagation training method using this function.getOutput.
WeightedConnection,
typically to support network learning.Nodes.Nodes to the connection.
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