1 |
P. Haghbayan, H. Nezamabadi-Pour, and S. Kamyab, A niche GSA method with nearest neighbor scheme for multimodal optimization, Swarm and Evolutionary Comput. 35 (2017), 78-93.
DOI
|
2 |
G. Xu, C. Xiu, and Z. Wan, Hysteretic chaotic operator network and its application in wind speed series prediction, Neurocomput. 165 (2015), 384-388.
DOI
|
3 |
Y. He et al., Reservoir flood control operation based on chaotic particle swarm optimization algorithm, Appl. Math. Model 38 (2014), no. 17-18, 4480-4492.
DOI
|
4 |
B. G. S. Dhas and S. N. Deepa, A hybrid PSO and GSA-based maximum power point tracking algorithm for, PV systems, in Proc. IEEE Int. Conf. Computat. Intell. Comput. Research (Enathi, India), 2013, pp. 1-4.
|
5 |
S. Jayaprakasam, S. K. A. Rahim, and C. Y. Leow, PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming, Appl. Soft Comput. J. 30 (2015), 229-237.
DOI
|
6 |
R. K. Khadanga and J. K. Satapathy, Unified power flow controller based damping controller design: - A hybrid PSO-GSA approach, in Proc. Int. Conf. Energy, Power Environ. (Shillong, India), 2015, pp. 1-6.
|
7 |
D. Yang et al., Application of BP neural network in image compression under the matlab, in Proc. Int. Conf. Comput. Syst., Electron. Contr. (Dalian, China), 2018, pp. 1081-1086.
|
8 |
N. Rajagopal and K. V. Prasad, Process framework for Smart Grid implementation, in Proc. Int. IEEE Innovative Smart Grid Technologies-Asia (Bangalore, India), 2013, pp. 1-5.
|
9 |
T. Sidhu, M. Kanabar, and P. Parikh, Configuration and performance testing of IEC 61850 GOOSE, in Proc. IEEE Int. Conf. Adv. Pow. Sys. Auto. Protection (Beijing, China), 2011, pp. 1384-1389.
|
10 |
J. Ko et al., A novel network modeling and evaluation approach for security vulnerability quantification in substation automation systems, IEICE Trans. Info. Syst. E96-D (2013), no. 9, 2021-2025.
DOI
|
11 |
Z. Kun et al., Group-based search in unstructured peer-to-peer, networks, in Proc. Int. IEEE Global Telecomm. Conf. (Honolulu, HI, USA), 2009, pp. 1-6.
|
12 |
M. Roughan et al., Spatio-temporal compressive sensing and internet traffic matrices (extended version), IEEE ACM Trans. Netw. 20 (2012), no. 3, 662-676.
DOI
|
13 |
G. Fusco et al., Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models, in Proc. Int. Conf. Models Technol. Intell. Trans. Syst. (Budapest, Hungary), 2015, pp. 93-101.
|
14 |
B. A. Solhmirzaei, S. Azadi, and R. Kazemi, Road profile estimation using wavelet neural network and 7-DOF vehicle dynamic systems, J. Mech. Sci. Technol. 26 (2012), no. 10, 3029-3036.
DOI
|
15 |
M. G. Kanabar, T. S. Sidhu, and M. R. D. Zadeh, Laboratory investigation of IEC 61850-9-2-based busbar and distance relaying with corrective measure for sampled value loss/delay, IEEE Trans. Power Delivery 26 (2011), no. 4, 2587-2595.
DOI
|
16 |
L. Yu, B. Chen, and J. Xiao, An integrated system of intrusion detection based on rough set and wavelet neural network, in Proc. Int. Conf. Natural Comput. (Haikou, China), 2007, pp. 194-199.
|
17 |
T. Eterovic et al., Data mining meets network analysis: traffic prediction models, in Proc. Int. Convention. Inf. Commun. Technol., Elec. Microelec. (Opatija, Croatia), 2014, pp. 1479-1484.
|
18 |
W. H. Lee et al., Discovering traffic bottlenecks in an urban network by spatiotemporal data mining on location-based services, IEEE Trans. Intell. Trans. Syst. 12 (2011), no. 4, 1047-1056.
DOI
|
19 |
J. Wang et al., Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach, in Proc. Int. IEEE Conf. Comput. Commun. (Atlanta, GA, USA), 2017, pp. 1-9.
|
20 |
Y. Zang et al., Wavelet transform processing for cellular traffic prediction in machine learning networks, in Proc. IEEE China Summit and Int. Conf. Signal Inf. Process (Chengdu, China), 2015, pp. 458-462.
|
21 |
S. Suthaharan, Big data classification: Problems and challenges in network intrusion prediction with machine learning, Performance Evaluation Rev. 41 (2014), no. 4, 70-73.
DOI
|
22 |
Q. M. Wang, A. W. Fan, and H. S. Shi, Network traffic prediction based on improved support vector machine, Int. J. Syst. Assurance Eng. Manag. 8 (2017), 1976-1980.
DOI
|
23 |
M. Deshpande and P. R. Bajaj. Performance analysis of support vector machine for traffic flow prediction, in Proc. Int. Conf. Global Trends Signal, Process. Inf. Comput. Commun (Jalgaon, India), 2016, pp. 126-129.
|
24 |
M. Santhosh, C. Venkaiah, and D. M. V. Kumar, Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction, Energy Convers Manage 168 (2018), 482-493.
DOI
|
25 |
J. C. Li et al., A link prediction method for heterogeneous networks based on BP neural network, Physica A 495 (2018), 1-17.
DOI
|
26 |
H. Sun, Y. Xiang, and Y. Guo, Classification Method of EEG Signals Based on Wavelet Neural Network, in Int. Conf. Bioinform. Biomedical Eng. (Beijing, China), 2009, pp. 1-4.
|
27 |
H. S. Wang, Y. N. Wang, and Y. C. Wang, Cost estimation of plastic injection molding parts through integration of PSO and BP neural network, Expert Syst. Appl. 40 (2013), no. 2, 418-428.
DOI
|
28 |
G. Ren et al., A modified Elman neural network with a new learning rate scheme, Neurocomput. 286 (2018), 11-18.
DOI
|
29 |
J. Adamowski and H. F. Chan, A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol. 407 (2011), no. 1-4, 28-40.
DOI
|
30 |
H. J. Yang and X. Hu, Wavelet neural network with improved genetic algorithm for traffic flow time series prediction, Optik 127 (2016), no. 19, 8103-8110.
DOI
|
31 |
M. Sameti, M. A. Jokar, and F. R. Astaraei, Prediction of solar stirling power generation in smart grid by GA-ANN model, Int. J. Comput. Applicat. Technol. 55 (2017), no. 2, 147-157.
DOI
|
32 |
W. Wang et al., Forecasting daily streamflow using hybrid ANN models, J. Hydrol. 324 (2006), no. 1-4, 383-399.
DOI
|
33 |
P. M. Watson and K. C. Gupta, Design and optimization of CPW circuits using EM-ANN models for CPW components, IEEE Trans. Microw. Theory Techn. 45 (1997), no. 12, 2515-2523.
DOI
|
34 |
Z. Jadidi et al., Flow-based anomaly detection using neural network optimized with GSA algorithm, in Proc. Int. Conf. Distribut. Comput. Syst. (Philadelphia, PA, USA), 2013, pp. 76-81.
|
35 |
X. Yuan et al., Short-term wind power prediction based on LSSVM-GSA model, Energy Convers Manag. 101 (2015), 393-401.
DOI
|