Using active learning in motor control

The expectations in information and computer technologies of people increasingly differentiated with developing technology. At first, computers be used just to make complex calculations and transfer information, no longer have had the ability to the face of various events decision-making and to learn. People's desire to produce systems capable of learning and decision making, has led to the concept of artificial intelligence. Design and control of the system possible to system modelling. One of many ways to the frequently referenced in produce intelligent systems is artificial neural networks. Neural networks of human decision making, relationship-building and machine learning capabilities to gain them for the mathematical models of nerve cells. In this thesis, solid modeling, and the axis of motion with four points of a robot arm motion with the positioning of the servo motors, the types of artificial neural networks learning as active learning, ie learning by doing simulation strategy is presented.