### Abstract

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.

Original language | English |
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Pages (from-to) | 2049-2057 |

Number of pages | 9 |

Journal | Pattern Recognition |

Volume | 37 |

Issue number | 10 |

DOIs | |

Publication status | Published - 2004 Oct 1 |

### Keywords

- Fuzzy inference
- Fuzzy modeling and rule extraction
- Machine learning
- Neural network

### ASJC Scopus subject areas

- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence

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## Cite this

*Pattern Recognition*,

*37*(10), 2049-2057. https://doi.org/10.1016/j.patcog.2004.04.003