526
P. Yang et al.
2. C. Liu, X. Wu, J. Mao et al., Acoustic emission signal processing for rolling bearing running
state assessment using compressive sensing. Mech. Syst. Signal Process. 91, 395–406 (2017)
3. M. Song, H. Song, S. Xiao, A study on fault diagnosis method of rolling bearing based on
wavelet packet and improved BP neural network. IOP Conf. Ser.: Mater. Sci. Eng. 274(1), 1–6
(2017)
4. D. Qing, A BP neural network optimisation method based on dynamical regularization. J.
Control Decis. 6(2), 111–121 (2019)
5. G. Ghosh, P. Mandal, S.C. Mondal, Modeling and optimization of surface roughness in keyway
milling using ANN, genetic algorithm, and particle swarm optimization. Int. J. Adv. Manuf.
Technol. 100(5), 1–20 (2017)
6. J. Wang, Q. Xu, Q. Li, Some remarks on the deterministic particle swarm optimization
algorithm. Math. Methods Appl. Sci. 41(5), 1–6 (2017)
7. K. Koteswara Rao, G.S.V.P. Rajuc, Reducing interactive fault proneness in software application
using genetic algorithm based optimal directed random testing. Int. J. Comput. Appl. 41(4),
1–10 (2018)
8. M. Abdel-Basset, A.N. Hessin, L. Abdel-Fatah, A comprehensive study of cuckoo-inspired
algorithms. Neural Comput. Appl. 29(2), 345–361 (2016)
9. X.S. Yang, S. Deb, in Cuckoo search via Levy Flights. Proceeding of World Congress on Nature
and Biologically Inspired Computing, India (2009), pp. 210–214
10. N. Wang, X. Zhu, Y. Chen, J. Tao, in Improved cuckoo search algorithm based RBF-NN
for modeling reactor-regenerator system. IECON 2017-43rd Annual Conference of the IEEE
Industrial Electronics Society (IEEE, Beijing, 2017), pp. 5447–5452
P. Yang et al.
2. C. Liu, X. Wu, J. Mao et al., Acoustic emission signal processing for rolling bearing running
state assessment using compressive sensing. Mech. Syst. Signal Process. 91, 395–406 (2017)
3. M. Song, H. Song, S. Xiao, A study on fault diagnosis method of rolling bearing based on
wavelet packet and improved BP neural network. IOP Conf. Ser.: Mater. Sci. Eng. 274(1), 1–6
(2017)
4. D. Qing, A BP neural network optimisation method based on dynamical regularization. J.
Control Decis. 6(2), 111–121 (2019)
5. G. Ghosh, P. Mandal, S.C. Mondal, Modeling and optimization of surface roughness in keyway
milling using ANN, genetic algorithm, and particle swarm optimization. Int. J. Adv. Manuf.
Technol. 100(5), 1–20 (2017)
6. J. Wang, Q. Xu, Q. Li, Some remarks on the deterministic particle swarm optimization
algorithm. Math. Methods Appl. Sci. 41(5), 1–6 (2017)
7. K. Koteswara Rao, G.S.V.P. Rajuc, Reducing interactive fault proneness in software application
using genetic algorithm based optimal directed random testing. Int. J. Comput. Appl. 41(4),
1–10 (2018)
8. M. Abdel-Basset, A.N. Hessin, L. Abdel-Fatah, A comprehensive study of cuckoo-inspired
algorithms. Neural Comput. Appl. 29(2), 345–361 (2016)
9. X.S. Yang, S. Deb, in Cuckoo search via Levy Flights. Proceeding of World Congress on Nature
and Biologically Inspired Computing, India (2009), pp. 210–214
10. N. Wang, X. Zhu, Y. Chen, J. Tao, in Improved cuckoo search algorithm based RBF-NN
for modeling reactor-regenerator system. IECON 2017-43rd Annual Conference of the IEEE
Industrial Electronics Society (IEEE, Beijing, 2017), pp. 5447–5452