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1. | School of Management, Northwestern Polytechnical University, Xi'an 710072, China, China |
2. | School of Information Engineering, Yulin University, Yulin 719000, China |
References:
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H. Ammari, J. Garnier and V. Jugnon, Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging,, Discrete and Continuous Dynamical Systems - Series S, 8 (2015), 389. Google Scholar |
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S. J. Birkinshaw and G. Parkin, A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts,, Journal of Hydroinformatics, 10 (2013), 127. Google Scholar |
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B. Bonnard, T. Combot and L. Jassionnesse, Integrability methods in the time minimal coherence transfer for Ising chains of three spins,, Discrete and Continuous Dynamical Systems - Series A, 35 (2015), 4095.
doi: 10.3934/dcds.2015.35.4095. |
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M. Costantiti, A. Farina and F. Zirilli, The fusion of different resolution SAR images,, in Proceedings of the IEEE. Vol. 85, (1997), 139.
doi: 10.1109/5.554214. |
[5] |
Z. Du, X. Chen and Z. Feng, Multiple positive periodic solutions to a predator-prey model with Leslie-Gower Holling-type II functional response and harvesting terms,, Discrete and Continuous Dynamical Systems - Series S, 7 (2014), 1203.
doi: 10.3934/dcdss.2014.7.1203. |
[6] |
F. Zhang, H.-F. Xue and D.-S. Xu, Big data cleaning algorithms in cloud computing,, International Journal of Online Engineering, 9 (2013), 77. Google Scholar |
[7] |
K. Zhang, H. Huang and H. Yang, A transformer fault diagnosis method integrating improved genetic algorithm with least square support vector machine,, Power System Technology, 34 (2010), 164. Google Scholar |
[8] |
J. Li, H. Liu and Q. Wang, Fast imaging of electromagnetic scatterers by a two-stage multilevel sampling method,, Discrete and Continuous Dynamical Systems - Series S, 8 (2015), 547. Google Scholar |
[9] |
M. Padilla, A. Perera, I. Montoliu, A. Chaudry, K. Persaud and S. Marco, Drift compensation of gas sensor array data by orthogonal signal correction,, Chemometrics and Intelligent Laboratory Systems, 100 (2010), 28.
doi: 10.1016/j.chemolab.2009.10.002. |
[10] |
C. Pohl and J. L. Van Genderen, Image fusionin remote sensing: Concepts, methods and applications,, International Journal of Remote Sensing, 19 (1999), 823. Google Scholar |
[11] |
V. M. Quiroga and I. Popescu, Cloud and cluster computing in uncertainty analysis of integrated flood models,, Journal of Hydroinformatics, 15 (2013), 55. Google Scholar |
[12] |
R. Hwang Ryol and M. fred Huber, A particle filter approach fro multi-target tracking intelligent sobots and systems,, Intelligent Robots and Systems, 11 (2007), 2753. Google Scholar |
[13] |
H. Schättler and U. Ledzewicz, Fields of extremals and sensitivity analysis for multi-input bilinear optimal control problems,, Discrete and Continuous Dynamical Systems - Series A, 35 (2015), 4611. Google Scholar |
[14] |
D. Tsujinishi and S. Abe, Fuzzy least squares support vector machines for multiclass problems,, Neural Networks, 16 (2003), 785.
doi: 10.1016/S0893-6080(03)00110-2. |
[15] |
B. Üstün and W. J. Melssen, Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization,, Analytical Chimica Acta (S0003-2670), 544 (2005), 0003. Google Scholar |
show all references
References:
[1] |
H. Ammari, J. Garnier and V. Jugnon, Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging,, Discrete and Continuous Dynamical Systems - Series S, 8 (2015), 389. Google Scholar |
[2] |
S. J. Birkinshaw and G. Parkin, A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts,, Journal of Hydroinformatics, 10 (2013), 127. Google Scholar |
[3] |
B. Bonnard, T. Combot and L. Jassionnesse, Integrability methods in the time minimal coherence transfer for Ising chains of three spins,, Discrete and Continuous Dynamical Systems - Series A, 35 (2015), 4095.
doi: 10.3934/dcds.2015.35.4095. |
[4] |
M. Costantiti, A. Farina and F. Zirilli, The fusion of different resolution SAR images,, in Proceedings of the IEEE. Vol. 85, (1997), 139.
doi: 10.1109/5.554214. |
[5] |
Z. Du, X. Chen and Z. Feng, Multiple positive periodic solutions to a predator-prey model with Leslie-Gower Holling-type II functional response and harvesting terms,, Discrete and Continuous Dynamical Systems - Series S, 7 (2014), 1203.
doi: 10.3934/dcdss.2014.7.1203. |
[6] |
F. Zhang, H.-F. Xue and D.-S. Xu, Big data cleaning algorithms in cloud computing,, International Journal of Online Engineering, 9 (2013), 77. Google Scholar |
[7] |
K. Zhang, H. Huang and H. Yang, A transformer fault diagnosis method integrating improved genetic algorithm with least square support vector machine,, Power System Technology, 34 (2010), 164. Google Scholar |
[8] |
J. Li, H. Liu and Q. Wang, Fast imaging of electromagnetic scatterers by a two-stage multilevel sampling method,, Discrete and Continuous Dynamical Systems - Series S, 8 (2015), 547. Google Scholar |
[9] |
M. Padilla, A. Perera, I. Montoliu, A. Chaudry, K. Persaud and S. Marco, Drift compensation of gas sensor array data by orthogonal signal correction,, Chemometrics and Intelligent Laboratory Systems, 100 (2010), 28.
doi: 10.1016/j.chemolab.2009.10.002. |
[10] |
C. Pohl and J. L. Van Genderen, Image fusionin remote sensing: Concepts, methods and applications,, International Journal of Remote Sensing, 19 (1999), 823. Google Scholar |
[11] |
V. M. Quiroga and I. Popescu, Cloud and cluster computing in uncertainty analysis of integrated flood models,, Journal of Hydroinformatics, 15 (2013), 55. Google Scholar |
[12] |
R. Hwang Ryol and M. fred Huber, A particle filter approach fro multi-target tracking intelligent sobots and systems,, Intelligent Robots and Systems, 11 (2007), 2753. Google Scholar |
[13] |
H. Schättler and U. Ledzewicz, Fields of extremals and sensitivity analysis for multi-input bilinear optimal control problems,, Discrete and Continuous Dynamical Systems - Series A, 35 (2015), 4611. Google Scholar |
[14] |
D. Tsujinishi and S. Abe, Fuzzy least squares support vector machines for multiclass problems,, Neural Networks, 16 (2003), 785.
doi: 10.1016/S0893-6080(03)00110-2. |
[15] |
B. Üstün and W. J. Melssen, Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization,, Analytical Chimica Acta (S0003-2670), 544 (2005), 0003. Google Scholar |
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