The nonlinearity of processes in control systems have drawbacks when we are modeling with classical techniques
of control. An alternative is to model systems as the process developed by the human brain, intelligent systems can
overcome the obstacles of the nonlinearity1. There are techniques such as neural networks, fuzzy logic, expert
systems and genetic algorithms2, combination of neural networks and fuzzy logic generates an intelligent hybrid system called neuro-fuzzy system3. A Neural network is a set of interconnected neurons to process information and
solve problems according the perceived stimulus and the acquired training1. Theory of fuzzy sets is focused in the form of human reasoning to decide the state of an event and to determine whether a decision is true or false through sets of fuzzy rules4.