**2. Static neural network**

Static neural network was the first and simplest type. It is a nonlooped network since it does not contain a feedback or delay connection [1]. It is a statistical regression tool which allows the approximation of any nonlinear function sufficiently regular. The neural architecture of this network is presented as shown in **Figure 1**. It imitates the structure of the biological neuron. It is composed of a set of layers. The hidden one allows to receive a variable number of inputs, and information is moved only from inputs directly through hidden layer to the output layer without cycles or loops. Each connection is associated with a synaptic weight w, which represents the strength of each connection. The negative weight inhibits its input, while the positive weight accentuates it.

**Figure 1.** Static neural architecture.
