A hybrid interval prediction model for the PQ index using a lower upper bound estimation-based extreme learning machine-Reference-Cited by-同舟云学术

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A hybrid interval prediction model for the PQ index using a lower upper bound estimation-based extreme learning machine

Published:2021-07-27 Issue:17 Volume:25 Page:11551-11571
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Zhu Yushan,Wang Xiaoling,Wang Jiajun,Xue Linli,Yu Jia,Chen Wenlong

Funder

National Key R&D Program of China

Yalong River Joint Funds of the National Natural Science Foundation of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-021-06025-4.pdf

Reference71 articles.

1. Baili J, Lahouar S, Hergli M et al (2009) ARTICLE IN PRESS NDT & E International GPR signal de-noising by discrete wavelet transform. NDT E Int 42:696–703. https://doi.org/10.1016/j.ndteint.2009.06.003

2. Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Phys D Nonlinear Phenom 110:43–50. https://doi.org/10.1016/S0167-2789(97)00118-8

3. Chu S-C, Tsai P, Pan J-S (2006) Cat Swarm Optimization. 854–858. https://doi.org/10.1007/978-3-540-36668-3_94

4. Cortés-Ibáñez JA, González S, Valle-Alonso JJ et al (2020) Preprocessing methodology for time series: An industrial world application case study. Inf Sci (ny) 514:385–401. https://doi.org/10.1016/j.ins.2019.11.027

5. Delafrouz H, Ghaheri A, Ghorbani MA (2018) A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction. Soft Comput 22:2205–2215. https://doi.org/10.1007/s00500-016-2480-8

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