Research on residual GM optimization based on PEMEA-BP correction
With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Scientific reports - 10(2020), 1 vom: 09. Dez., Seite 21540 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Duan, Junhang [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 21.12.2020 Date Revised 30.03.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1038/s41598-020-77630-w |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM318644096 |
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| 500 | |a Date Revised 30.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Xing, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Zhong |e verfasserin |4 aut | |
| 700 | 1 | |a Gou, Huating |e verfasserin |4 aut | |
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