Wavelet neural network with improved genetic algorithm for traffic flow time series prediction
In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication mod...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Hong-jun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment - Cheng, Cheng ELSEVIER, 2020, international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy, München |
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| Übergeordnetes Werk: |
volume:127 ; year:2016 ; number:19 ; pages:8103-8110 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.ijleo.2016.06.017 |
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ELV014680599 |
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| 520 | |a In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. | ||
| 520 | |a In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. | ||
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10.1016/j.ijleo.2016.06.017 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000940.pica (DE-627)ELV014680599 (ELSEVIER)S0030-4026(16)30638-6 DE-627 ger DE-627 rakwb eng 333.7 VZ 43.00 bkl Yang, Hong-jun verfasserin aut Wavelet neural network with improved genetic algorithm for traffic flow time series prediction 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. Traffic flow forecast Elsevier Wavelet neural network Elsevier Parameter optimization Elsevier Improved genetic algorithm Elsevier Hu, Xu oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:127 year:2016 number:19 pages:8103-8110 extent:8 https://doi.org/10.1016/j.ijleo.2016.06.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 127 2016 19 8103-8110 8 |
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10.1016/j.ijleo.2016.06.017 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000940.pica (DE-627)ELV014680599 (ELSEVIER)S0030-4026(16)30638-6 DE-627 ger DE-627 rakwb eng 333.7 VZ 43.00 bkl Yang, Hong-jun verfasserin aut Wavelet neural network with improved genetic algorithm for traffic flow time series prediction 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. Traffic flow forecast Elsevier Wavelet neural network Elsevier Parameter optimization Elsevier Improved genetic algorithm Elsevier Hu, Xu oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:127 year:2016 number:19 pages:8103-8110 extent:8 https://doi.org/10.1016/j.ijleo.2016.06.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 127 2016 19 8103-8110 8 |
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10.1016/j.ijleo.2016.06.017 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000940.pica (DE-627)ELV014680599 (ELSEVIER)S0030-4026(16)30638-6 DE-627 ger DE-627 rakwb eng 333.7 VZ 43.00 bkl Yang, Hong-jun verfasserin aut Wavelet neural network with improved genetic algorithm for traffic flow time series prediction 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. Traffic flow forecast Elsevier Wavelet neural network Elsevier Parameter optimization Elsevier Improved genetic algorithm Elsevier Hu, Xu oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:127 year:2016 number:19 pages:8103-8110 extent:8 https://doi.org/10.1016/j.ijleo.2016.06.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 127 2016 19 8103-8110 8 |
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10.1016/j.ijleo.2016.06.017 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000940.pica (DE-627)ELV014680599 (ELSEVIER)S0030-4026(16)30638-6 DE-627 ger DE-627 rakwb eng 333.7 VZ 43.00 bkl Yang, Hong-jun verfasserin aut Wavelet neural network with improved genetic algorithm for traffic flow time series prediction 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. Traffic flow forecast Elsevier Wavelet neural network Elsevier Parameter optimization Elsevier Improved genetic algorithm Elsevier Hu, Xu oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:127 year:2016 number:19 pages:8103-8110 extent:8 https://doi.org/10.1016/j.ijleo.2016.06.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 127 2016 19 8103-8110 8 |
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10.1016/j.ijleo.2016.06.017 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000940.pica (DE-627)ELV014680599 (ELSEVIER)S0030-4026(16)30638-6 DE-627 ger DE-627 rakwb eng 333.7 VZ 43.00 bkl Yang, Hong-jun verfasserin aut Wavelet neural network with improved genetic algorithm for traffic flow time series prediction 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. Traffic flow forecast Elsevier Wavelet neural network Elsevier Parameter optimization Elsevier Improved genetic algorithm Elsevier Hu, Xu oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:127 year:2016 number:19 pages:8103-8110 extent:8 https://doi.org/10.1016/j.ijleo.2016.06.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 127 2016 19 8103-8110 8 |
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Wavelet neural network with improved genetic algorithm for traffic flow time series prediction |
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Wavelet neural network with improved genetic algorithm for traffic flow time series prediction |
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Yang, Hong-jun |
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Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment |
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Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment |
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Yang, Hong-jun |
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wavelet neural network with improved genetic algorithm for traffic flow time series prediction |
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Wavelet neural network with improved genetic algorithm for traffic flow time series prediction |
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In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. |
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In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. |
| abstract_unstemmed |
In the traditional wavelet neural network (WNN) prediction model, the parameter optimization is performed using a unidirectional gradient descent algorithm, which has the problems of slow convergence and local optimum. To improve the predication accuracy of short-term traffic flow, a predication model based on clustering search strategy improved genetic algorithm (IGA) and WNN (IGA-WNN) is proposed. The IGA is used to optimize the initial connection weights, translation factor and scaling factor of WNN. The algorithm is applied to the short-term traffic flow of empirical research. The experimental results show that IGA-WNN model has a higher predication accuracy and a better nonlinear fitting ability compared with the traditional WNN and GA-WNN prediction models. |
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Wavelet neural network with improved genetic algorithm for traffic flow time series prediction |
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https://doi.org/10.1016/j.ijleo.2016.06.017 |
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Hu, Xu |
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