E. G. Carayannis, F. J. David, and M. P. Efthymiopoulos, “Cyber-Development, Cyber-Democracy and Cyber-Defense Challenges, Opportunities and Implications for Theory, Policy and Practice,” Springer Science Business and Media, pp. 5-22, 2014.
M. R. Mosavi, M. Khishe and M. Aghababaie, “Modeling and Mitigation of Active Sonar Clutter”, Noshahr University of Marine Science and Technology, 2015. (In Persian)
M. R. Mosavi, M. Khishe, A. Ghamgosar and M. J. Ghalandari, “Classification of Sonar Data Set using the Gray Wolf Optimizer Algorithm”, Journal of Electronics Industries, Vol.7, No.1, pp.27-41, 1395. (In Persian)
M. R. Mosavi, M. Khishe and E. Ebrahimi, “Classification of Sonar Targets using OMKC, Genetic Algorithm and Statistical Moments,” Journal of Advances in Computer Research, vol. 7, no. 1, pp. 143-156, 2016.
V. Abedifar, M. Eshghi, S. Mirjalili, and S. M. Mirjalili, “An Optimized Virtual Network Mapping using PSO in Cloud Computing,” 21st Iranian Conference on Electrical Engineering, pp. 1-6, 2013.
L. S. Nguyen, D. Frauendorfer, M. S. Mast, and D. Gatica-Perez, “Hire Me: Computational Inference of Hirability in Employment Interviews based on Nonverbal Behavior,” IEEE Transactions on Multimedia, vol. 16, no. 4, pp. 1018-1031, 2014.
P. Auer, H. Burgsteiner, and W. Maass, “A Learning Rule for Very Simple Universal Approximators Consisting of a Single Layer of Perceptrons,” Neural Networks, vol. 21, no. 5, pp. 786-795, June 2008.
J. Moody and C. J. Darken, “Fast Learning in Networks of Locally-Tuned Processing Units,” Neural Computation, vol. 1, no. 2, pp. 281-294, 1989.
N. Karayiannis, “Reformulated Radial Basis Neural Networks Trained by Gradient Descent,” IEEE Transactions on Neural Networks, vol. 10, no.3, pp. 657-671, 1999.
C. Liu, H. Wang, and P. Yao, “On Terrain-Aided Navigation for Unmanned Aerial Vehicle using B-spline Neural Network and Extended Kalman Filter,” IEEE Conference on Guidance, Navigation and Control (CGNCC), pp. 2258- 2263, 2014.
D. Simon, “Training Radial Basis Neural Networks with the Extended Kalman Filter,” Neurocomputing, vol. 48, no. 1-4, pp. 455-475, 2002.
Q. Zhang and B. Li, “A Low-Cost GPS/INS Integration Based on UKF and BP Neural Network,” IEEE Conference on
Intelligent Control and Information Processing (ICICIP), pp. 100-107, 2014.
X. Li, T. Zhang, Z. Deng, and J. Wang, “A Recognition Method of Plate Shape Defect Based on RBF-BP Neural Network Optimized by Genetic Algorithm,” IEEE Conference on Control and Decision, pp. 3992-3996, 2014.
K. S. Narendra and M. A. L. Thathachar, “Learning Automata: An Introduction,” Prentice-Hall, Englewood Cliffs, NJ, 1989.
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, New Series, vol. 220, no. 4598, pp. 671-680, 1983.
C. Ozturk and D. Karaboga, “Hybrid Artificial Bee Colony Algorithm for Neural Network Training,” IEEE Congress on Evolutionary Computation (CEC 2011), pp. 84-88, 2011.
J. J. Yu, A.Y. Lam, and V. O. Li, “Evolutionary Artificial Neural Network based on Chemical Reaction Optimization,” IEEE Congress on Evolutionary Computation (CEC 2011), pp. 2083-2090, 2011.
S. Mirjalili and A. S. Sadiq, “Magnetic Optimization Algorithm for Training Multi-Layer Perceptron,” IEEE Conference on Communication Software and Networks (ICCSN 2011), pp. 42-46, 2011.
R. C. Green, L. Wang, and M. Alam, “Training Neural Networks Using Central Force Optimization and Particle Swarm Optimization: Insights and Comparisons,” Expert System with Application, vol. 39, no. 1, pp. 555-563, 2012.
P. Moallem and N. Razmjooy, “A Multi-Layer Perceptron Neural Network Trained by Invasive Weed Optimization for Potato Color Image Segmentation,” Trends in Applied Sciences Research, vol. 7, no. 6, pp. 445-455, 2012.
L. A. Pereira, L. C. Afonso, J. P. Papa, Z. A. Vale, C. C. Ramos, D. S. Gastaldello, and A. N. Souza, “Multilayer Perceptron Neural Networks Training Through Charged System Search and Its Application for Non-Technical Losses Detection on Innovative Smart Grid Technologies,” IEEE PES Conference on Latin America (ISGT LA 2013), pp. 1-6, 2013.
L. Pereira, D. Rodrigues, P. Ribeiro, J. Papa, and S. A. Weber, “Social-Spider Optimization-Based Artificial Neural Networks Training and its Applications for Parkinson’s Disease Identification,” IEEE Symposium on Computer-based Medical Systems (CBMS 2014), pp. 14-17, 2014.
E. Uzlu, M. Kankal, A. Akpınar, and T. Dede, “Estimates of Energy Consumption in Turkey using Neural Networks with the Teaching-Learning-based Optimization Algorithm,” nergy, vol. 75, pp. 295-303, 2014.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Let a Biogeography-based Optimizer Train Your Multi-Layer Perceptron,” Journal of Information Sciences, vol. 269, pp. 188-209, June 2014.
N. Muangkote, K. Sunat, and S. Chiewchanwattana, “An Improved Grey Wolf Optimizer for Training q-Gaussian Radial Basis Functional-link Nets,” 2014 International Computer Science and Engineering Conference (ICSEC), pp. 209-214, 2014.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
M. R. Mosavi, M. Khishe, and A. Moridi, “Sonar Dataset Classification using Hybrid PSO-GSA Method,” Marine Technology, vol. 3, no. 1, pp. 1-14, 2016.
O. Olorunda and A. P. Engelbrecht, “Measuring Exploration/Exploitation in Particle Swarms using Swarm Diversity,” IEEE World Congress on Computational Intelligence, pp. 1128-1134, 2008.
L. Lin and M. Gen, “Auto-Tuning Strategy for Evolutionary Algorithms: Balancing between Exploration and Exploitation,” Soft Computing, vol. 13, no. 2, pp. 157-168, 2009.
S. Mirjalili, S. Z. M. Hashim, and H. M. Sardroudi, “Training Feedforward Neural Networks using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm,” Applied Mathematics and Computation, vol. 218, no. 22, pp. 11125-11137, 2012.
R. E. Precup, R. C. David, E. M. Petriu, and M. B. Radac, “Adaptive GSA-Based Optimal Tuning of PI Controlled Servo Systems with Reduced Process Parametric Sensitivity, Robust Stability and Controller Robustness,” IEEE Transactions on Cybernetics, vol. 44, no. 11, pp. 1997-2009, 2014.
B. Yu and X. He, “Training Radial Basis Function Networks with Differential Evolution,” IEEE Conference on Granular Computing, pp. 369-372, 2006.
M. Gauci, T. J. Dodd, and R. Groß, “Why ‘GSA: A Gravitational Search Algorithm’ is not Genuinely based on the Law of Gravity,” Natural Computing, vol. 11, no. 4, pp. 719-720, 2012.
E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “BGSA: Binary Gravitational Search Algorithm,” Natural Computing, vol. 9, no. 3, pp. 727-745, 2010.
R. P. Gorman and T. J. Sejnowski, “Analysis of Hidden Units in a Layered Network Trained to Classify Neural Networks, vol. 1, pp. 75-89, 1988.