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 regimeswitching 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 a...

• Type: Journal
• Year: 2010
• Where: TNN
• Authors: José Luis Aznarte, José Manuel Benítez
• Keywords: Artificial Neural Networks, Fuzzy Rule-based Systems, Management, Time Series, TNN 2010