Neuro-fuzzy modelling and control of nonlinear dynamic systems
G. Quadrelli, Ricardo Tanscheit, Marley M. B. R. Vellasco · Learning and Nonlinear Models · 2003
The main goal of this paper is to propose procedures for modelling and control of nonlinear systems by using neuro-fuzzy topologies.For the modelling of a nonlinear system, its input space is initially divided into a number of fuzzy operating regions, within which reduced order models represent the system's behaviour.The complete system modelling -the global model -is obtained through the conjunction of the local models by using a neuro-fuzzy network.A neuro-fuzzy adaptive network, based on a hybrid learning algorithm (self-organised learning and supervised learning) and called FALCON-H, is used in the control of a nonlinear plant modelled as described above.