期刊
CHAOS SOLITONS & FRACTALS
卷 130, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chaos.2019.109444
关键词
Fractional calculus; Neural networks; Black box modeling; System identification
资金
- CONACyT
- SNI-CONACyT
Neural networks and fractional order calculus have shown to be powerful tools for system identification. In this paper we combine both approaches to propose a fractional order neural network (FONN) for system identification. The learning algorithm was generalized considering the Grunwald-Letnikov fractional derivative. This new black box modeling approach is validated by the identification of three different systems (two benchmark systems and a real system). Comparisons vs others approaches showed that the proposed FONN model reached better accuracy with less number of parameters. (C) 2019 Elsevier Ltd. All rights reserved.
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