How to improve fuzzy-neural system modeling by means of qualitative simulation

R. Bellazzi, R. Guglielmann, L. Ironi

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

Abstract

The main problem in efficiently building robust fuzzy-neural models of nonlinear systems lies in the difficulty to define a "meaningful" fuzzy rule-base. Our approach to the solution of such a problem is based on a hybrid method which integrates fuzzy systems with qualitative models. We introduce qualitative models to exploit the available, although incomplete, a priori physical knowledge on the system with the goal to infer, through qualitative simulation, all of its possible behaviors. We show here that a rule-base, which captures all of the distinctions in the system states, is automatically generated by encoding the knowledge of the system dynamics described by the outcomes of its qualitative simulation. Such a rule-base properly initializes a fuzzy identifier, which is then tuned to a set of experimental data. Our method has shown good performance when applied both as a predictor and as a simulator.

Lingua originaleInglese
pagine (da-a)249-253
Numero di pagine5
RivistaIEEE Transactions on Neural Networks
Volume11
Numero di pubblicazione1
DOI
Stato di pubblicazionePubblicato - gen 2000
Pubblicato esternamente

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