## Abstract

In this paper, the uncertainty property is represented by Z-number as the coefficients and variables of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. Here, we use fuzzy equations as the models for the uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy equation. However, it is very difficult to obtain Z-number coefficients of the fuzzy equations. Taking into consideration the modeling case at par with uncertain nonlinear systems, the implementation of neural network technique is contributed in the complex way of dealing the appropriate coefficients of the fuzzy equations. We use the neural network method to approximate Z-number coefficients of the fuzzy

equations.

equations.

Original language | English |
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Title of host publication | Advances in Computational Intelligence Systems |

Subtitle of host publication | Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK |

Editors | Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity |

Publisher | Springer |

Pages | 96-107 |

ISBN (Electronic) | 978-3-319-97982-3 |

ISBN (Print) | 978-3-319-97981-6 |

DOIs | |

Publication status | Published - Sept 2018 |

Event | 18th UK Workshop on Computational Intelligence - Nottingham, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018 |

### Publication series

Name | Advances in Intelligent Systems and Computing |
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Publisher | Springer |

Volume | 840 |

ISSN (Print) | 2194-5357 |

ISSN (Electronic) | 2194-5365 |

### Workshop

Workshop | 18th UK Workshop on Computational Intelligence |
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Country/Territory | United Kingdom |

Period | 5/09/18 → 7/09/18 |

## Keywords

- Fuzzy Modeling
- Z-number
- Uncertain Nonlinear System