init.MLPneuron: Neuron constructor.
In AMORE: Artificial Neural Network Training and Simulating

Description Usage Arguments Value Author(s) See Also

Creates a neuron according to the structure established by the AMORE package standard.

1 2	init.MLPneuron(id, type, activation.function, output.links, output.aims, input.links, weights, bias, method, method.dep.variables)

`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, the third, ..., element of the output vector?
`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.
`bias`	The bias summed to the weighted sum of the inputs.
`method`	Prefered training method. Currently it can be: "ADAPTgd": Adaptative gradient descend. "ADAPTgdwm": Adaptative gradient descend with momentum. "BATCHgd": BATCH gradient descend. "BATCHgdwm": BATCH gradient descend with momentum.
`method.dep.variables`	Variables used by the training methods: ADAPTgd method: delta: Used in the backpropagation method. learning.rate: Learning rate parameter. Notice that we can use a different rate for each neuron. ADAPTgdwm method: delta: Used in the backpropagation method. learning.rate: Learning rate parameter. Notice that we can use a different rate for each neuron. momentum: Momentum constant used in the backpropagation with momentum learning criterium. former.weight.change: Last increment in the weight parameters. Used by the momentum training technique. former.bias.change: Last increment in the bias parameter. Used by the momentum training technique. BATCHgd method: delta: Used in the backpropagation method. learning.rate: Learning rate parameter. Notice that we can use a different rate for each neuron. sum.delta.x: Used as an acumulator of the changes to apply to the weight parameters in the batch training. sum.delta.bias: Used as an acumulator of the changes to apply to the bias parameters in the batch training. BATCHgdwm method: delta: Used in the backpropagation method. learning.rate: Learning rate parameter. Notice that we can use a different rate for each neuron. sum.delta.x: Used as an acumulator of the changes to apply to the weight parameters in the batch training. 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. former.weight.change: Last increment in the weight parameters. Used by the momentum training technique. former.bias.change: Last increment in the bias parameter. Used by the momentum training technique.