Ortíz Rodríguez, José Manuel; Martínez Blanco, María del Rosario; Vega Carrillo, Héctor René
Resumen:
Artificial Neural Networks (ANN), are highly simplified models of the brain processes
(Graupe, 2007; Kasabov, 1998). AnANNis a biologically inspired computational model which
consists of a large number of simple processing elements called neurons, units, cells, or nodes
which are interconnected and operate in parallel (Galushkin, 2007; Lakhmi & Fanelli, 2000).
Each neuron is connected to other neurons by means of directed communication links, which
constitute the neuronal structure, each with an associated weight (Dreyfus, 2005). The weights
represent information being used by the net to solve a problem. Figure 1 shows an abbreviated
notation for an individual artificial neuron, which is used in schemes of multiple neurons
(Beale et al., 1992). Here the input p, a vector of R input elements, is represented by the solid
dark vertical bar at the left. The dimensions of p are shown below the symbol p in the figure
as Rx1. These inputs post multiply the single-row, R − column matrix W. A constant 1 enters
the neuron as an input and is multiplied by a bias b. The net input to the transfer function f is
n, the sum of the bias b and the product Wp. This sum is passed to the transfer function f to
get the neuron’s output a.