title: Equivalences between neural-autoregressive time series models and fuzzy systems.
abstract: Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutionary algorithms, probabilistic reasoning). Since its inception, a distinctive goal has been to dig out the deep relationships among their components. This paper considers two wide families of SC models. On the one hand, the regime-switching autoregressive paradigm is a recent development in statistical time series modeling, and it includes a set of models closely related to artificial neural networks. On the other hand, we consider fuzzy rule-based systems in the framework of time series analysis. This paper discloses original results establishing functional equivalences between models of these two classes, and hence opens the door to a productive line of research where results and techniques from one area can be applied in the other. As a consequence of the equivalences presented in this paper, we prove the asymptotic stationarity of a class of fuzzy rule-based systems. Simulations based on information criteria show the importance of the selection of the proper membership function.
authors.affiliation: Center for Energy and Processes of MINES ParisTech, France. jlaznarte@decsai.ugr.es
authors.forename: José Luis
authors.initials: JL
authors.lastname: Aznarte
authors.forename: José Manuel
authors.initials: JM
authors.lastname: Benitez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D013997:Time Factors