### 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.

Original language | English |
---|---|

Pages (from-to) | 249-253 |

Number of pages | 5 |

Journal | IEEE Transactions on Neural Networks |

Volume | 11 |

Issue number | 1 |

DOIs | |

Publication status | Published - Jan 2000 |

### Fingerprint

### Keywords

- 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

### Cite this

*IEEE Transactions on Neural Networks*,

*11*(1), 249-253. https://doi.org/10.1109/72.822528

**How to improve fuzzy-neural system modeling by means of qualitative simulation.** / Bellazzi, R.; Guglielmann, R.; Ironi, L.

Research output: Contribution to journal › Article

*IEEE Transactions on Neural Networks*, vol. 11, no. 1, pp. 249-253. https://doi.org/10.1109/72.822528

}

TY - JOUR

T1 - How to improve fuzzy-neural system modeling by means of qualitative simulation

AU - Bellazzi, R.

AU - Guglielmann, R.

AU - Ironi, L.

PY - 2000/1

Y1 - 2000/1

N2 - 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.

AB - 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.

KW - Fuzzy systems

KW - Identification

KW - Neural networks

KW - Qualitative simulation

UR - http://www.scopus.com/inward/record.url?scp=0033640722&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033640722&partnerID=8YFLogxK

U2 - 10.1109/72.822528

DO - 10.1109/72.822528

M3 - Article

C2 - 18249757

AN - SCOPUS:0033640722

VL - 11

SP - 249

EP - 253

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

SN - 1045-9227

IS - 1

ER -