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

R. Bellazzi, R. Guglielmann, L. Ironi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)249-253
Number of pages5
JournalIEEE Transactions on Neural Networks
Issue number1
Publication statusPublished - Jan 2000


  • Fuzzy systems
  • Identification
  • Neural networks
  • Qualitative simulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture


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