Journal of Mathematical Sciences: Advances and Applications[1] Mohammad Ali Ghorbani, Ozgur Kisi and Mahdi Aalinezhad, A probe
into the chaotic nature of daily streamflow time series by correlation
dimension and largest Lyapunov methods, Applied Mathematical Modelling
34(12) (2010) 4050-4057.
DOI: https://doi.org/10.1016/j.apm.2010.03.036
[2] Anuradha Yarlagadda, J. V. R. Murthy and M. H. M. Krishna Prasad,
Estimating correlation dimension using multi layered grid and damped
window model over data streams, Procedia Technology 10 (2013),
797-804.
DOI: https://doi.org/10.1016/j.protcy.2013.12.424
[3] Chao Ren, Ning An, Jianzhou Wang, Lian Li, Bin Hu and Duo Shang,
Optimal parameters selection for BP neural network based on particle
swarm optimization: A case study of wind speed forecasting,
Knowledge-Based Systems 56 (2014), 226-239.
DOI: https://doi.org/10.1016/j.knosys.2013.11.015
[4] Jun Wang, Zheng Sheng, Bihua Zhou and Shudao Zhou, Lightning
potential forecast over Nanjing with denoised sounding-derived indices
based on SSA and CS-BP neural network, Atmospheric Research 137
(2014), 245-256.
DOI: https://doi.org/10.1016/j.atmosres.2013.10.014
[5] Li Zhuo, Jing Zhang, Pei Dong, Yingdi Zhao and Bo Peng, An
SA-GA-BP neural network-based color correction algorithm for TCM
tongue images, Neurocomputing 134 (2014), 111-116.
DOI: https://doi.org/10.1016/j.neucom.2012.12.080
[6] Xue Feng Zhao, Qin Ba, Lei Zhou, Weijie Li and Jinping Ou, BP
neural network recognition algorithm for scour monitoring of subsea
pipelines based on active thermometry, Optik-International Journal for
Light and Electron Optics 125(18) (2014), 5426-5431.
DOI: https://doi.org/10.1016/j.ijleo.2014.06.018
[7] Feng Yu and Xiaozhong Xu, A short-term load forecasting model of
natural gas based on optimized genetic algorithm and improved BP
neural network, Applied Energy 134 (2014), 102-113.
DOI: https://doi.org/10.1016/j.apenergy.2014.07.104
[8] Yanxi Zhang, Xiangdong Gao and Seiji Katayama, Weld appearance
prediction with BP neural network improved by genetic algorithm during
disk laser welding, Journal of Manufacturing Systems 34 (2015),
53-59.
DOI: https://doi.org/10.1016/j.jmsy.2014.10.005
[9] Dongsheng Guo, Yunong Zhang, Zhengli Xiao, Mingzhi Mao and Jianxi
Liu, Common nature of learning between BP-type and Hopfield-type
neural networks, Neurocomputing 167 (2015), 578-586.
DOI: https://doi.org/10.1016/j.neucom.2015.04.032
[10] Yingjie Xu, Tao You and Chenglie Du, An integrated
micromechanical model and BP neural network for predicting elastic
modulus of 3-D multi-phase and multi-layer braided composite,
Composite Structures 122 (2015), 308-315.
DOI: https://doi.org/10.1016/j.compstruct.2014.11.052
[11] Xiao Wei Li, Sung Jin Cho and Seok Tae Kim, Combined use of BP
neural network and computational integral imaging reconstruction for
optical multiple-image security, Optics Communications 315 (2014),
147-158.
DOI: https://doi.org/10.1016/j.optcom.2013.11.003
[12] A. Ismail, D.-S. Jeng and L. L. Zhang, An optimized product-unit
neural network with a novel PSO-BP hybrid training algorithm:
Applications to load-deformation analysis of axially loaded piles,
Engineering Applications of Artificial Intelligence 26(10) (2013),
2305-2314.
DOI: https://doi.org/10.1016/j.engappai.2013.04.007
[13] Hadley Brooks and Nick Tucker, Electrospinning predictions using
artificial neural networks, Polymer 58 (2015), 22-29.
DOI: https://doi.org/10.1016/j.polymer.2014.12.046
[14] Komeil Nasouri, Hossein Bahrambeygi, Amir Rabbi, Ahamd Mousavi
Shoushtari and Ali Kaflou, Modeling and optimization of electrospun
PAN nanofiber diameter using response surface methodology and
artificial neural network, Journal of Applied Polymer Science 126(1)
(2012), 127-135.
DOI: ttps://doi.org/10.1002/app.36726
[15] K. Sarkar, M. B. Ghalia, Z. Wu and S. C. Bose, A neural network
model for the numerical prediction of the diameter of electrospun
polyethylene oxide nanofibers, Journal of Materials Processing
Technology 209(7) (2009), 3156-3165.
DOI: https://doi.org/10.1016/j.jmatprotec.2008.07.032
[16] A. S. Nateri and M. Hasanzadeh, Using fuzzy-logic and neural
network techniques to evaluating polyacrylonitrile nanofiber diameter,
Journal of Computational and Theoretical Nanoscience 6(7) (2009),
1542-1545.
DOI: https://doi.org/10.1166/jctn.2009.1208
