Título: Neuro-fuzzy modelling and control of nonlinear dynamic systems
Autores: Quadrelli, G.; Tanscheit, R.; Vellasco, M. M.
Resumo: 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.
Palavras-chave: Neuro-fuzzy systems; nonlinear systems; modelling; control
Páginas: 9
Código DOI: 10.21528/lmln-vol1-no1-art1
Artigo em PDF: vol1-no1-art1.pdf
Arquivo BibTex: vol1-no1-art1.bib