RBF neural network controller for nonlinear systems

This paper presents a RBF neural network based controller used in commanding time varying systems with uncertainties task. First, a reduction procedure of the initial set of parameters using an unsupervised pattern recognition technique was applied. After this, an RBF neural network was trained using the minimized set of data obtained above. The advantage of this method is overcoming the difficulties implied by the direct solving of the differential models, which are necessary in a classical approach. An application of missile-target tracking was implemented using the mentioned method, and the results are compared with those obtained in a classical approach