Equivalences between neural-autoregressive time series models and fuzzy systems.
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.
Aznarte
José Luis
JL
Center for Energy and Processes of MINES ParisTech, France. jlaznarte@decsai.ugr.es
Benitez
José Manuel
JM
D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D013997:Time Factors