Improved CS Algorithm and its Application in Parking Space Prediction
Abstract This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network (WNN) model, and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search (CS) algorithm. First, the initialization parameters are provided to optimize the WNN using the improved CS. The traditional CS algorithm adopts the strategy of overall update and evaluation, but does not consider its own information, so the convergence speed is very slow. The proposed algorithm employs the evaluation strategy of group update, which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy, but also increases the mutual relationship between the nests and reduces the overall running time. Then, we use the WNN model to predict parking information. The proposed algorithm is compared with six different heuristic algorithms in five experiments. The experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Journal of bionic engineering - 17(2020), 5 vom: 24. Juni, Seite 1075-1083 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Rui [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: |
50.14 / Technik in Beziehung zu anderen Gebieten / Technik in Beziehung zu anderen Gebieten |
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| Themen: |
Available parking spaces prediction |
| Anmerkungen: |
© Jilin University 2020 |
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| doi: |
10.1007/s42235-020-0056-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
OLC2119276129 |
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| 520 | |a Abstract This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network (WNN) model, and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search (CS) algorithm. First, the initialization parameters are provided to optimize the WNN using the improved CS. The traditional CS algorithm adopts the strategy of overall update and evaluation, but does not consider its own information, so the convergence speed is very slow. The proposed algorithm employs the evaluation strategy of group update, which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy, but also increases the mutual relationship between the nests and reduces the overall running time. Then, we use the WNN model to predict parking information. The proposed algorithm is compared with six different heuristic algorithms in five experiments. The experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy. | ||
| 650 | 4 | |a wavelet neural network | |
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| 700 | 1 | |a Kang, Hui |4 aut | |
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