Usage
init.neuron(id,type,activation.function,output.links, output.aims, input.links, weights, bias, former.weight.change, former.bias.change, learning.rate, sum.delta.x, sum.delta.bias, momentum, delta)
Arguments
id
Numerical index of the neuron (so as to be refered in a network operation).
type
Either hidden or ouput, according to the layer the neuron belongs to.
activation.function
The name of the characteristic function of the neuron. It can be "pureline", "tansig", "sigmoid" or even "custom" in case that the user wants to configure its own activation function accordingly defining f0 and f1.
output.links
The id's of the neurons that accept the output value of this neuron as an input.
output.aims
The location of the output of the neuron in the input set of the addressed neuron. Gives answer to: Is this output the first, the second, the third, ..., input at the addressed neuron?. Similarly for an output neuron: Is this output the first, the second,
input.links
The id's of the neurons whose outputs work as inputs for this neuron. Positive values represent that we take the outputs of other neurons as inputs. Negative values represent the coordinates of the input vector to be considered as inputs.
weights
The multiplying factors of the input values.
former.bias.change
Last increment in the bias parameter. Used by the momentum training technique.
learning.rate
Learning rate parameter. Notice that we can use a different rate for each neuron.
sum.delta.bias
Used as an acumulator of the changes to apply to the bias parameters in the batch training.
momentum
Momentum constant used in the backpropagation with momentum learning criterium.
delta
Used in the backpropagation method.