- Bazaraa, M. S., Sherali, H. D. & Shetty, C. M. (2005). Nonlinear Programming. John Wiley & Sons, Inc.
- Boutselis, P. & Ringrose, T.J. (2013). GAMLSS and neural networks in combat simulation metamodelling: A case study. Expert Systems with Applications, 40(15): 6087-6093.
- Bouzerdoum, A. & Pattison, T. R. (1993). Neural network for quadratic optimization with bound constraints. IEEE Transactions on Neural Networks, 4(2): 293–304.
- Effati, S. & Moghaddas, M. (2016). A novel neural network based on NCP function for solving constrained nonconvex optimization problems. Complexity, 21(6):130–141.
- Effati, S., Ghomashi, A., & Abbasi, M. (2011). A novel recurrent neural network for solving MLCPs and its application to linear and quadratic programming. Asia Pacific Journal of Operational Research, 28 (4): 523–541.
- Gao, X. & Liao, L. Z. (2010). A new one-layer neural network for linear and quadratic programming. IEEE Transactions on Neural Networks, 21(6): 918–929.
- Kennedy, M. & Chua, L. (1988). Neural networks for nonlinear programming. IEEE Transactions on Circuits and Systems, 35(5): 554–562.
- Kilmer, R. A. (1996). Applications of artificial neural networks to combat simulations. Mathematical and Computer Modelling, 23(1–2): 91-99.
- Law, A. & Kelton, W. (1991). Simulation Modeling and Analysis, McGraw-Hill, New York.
- Maa, C. Y. & Shanblatt, M. A. (1992). A two-phase optimization neural network. IEEE Transactions on Neural Networks, 3(6):1003–1009.
- Miller, R. & Michel, A. (1982). Ordinary Differential Equations. Academic Press, Inc.
- Nazemi, A. & Effati, S. (2013). An application of a merit function for solving convex programming problems. Computers & Industrial Engineering, 66(2): 212–221.
- Nazemi, A. (2012). A dynamic system model for solving convex nonlinear optimization problems. Communications in Nonlinear Science and Numerical Simulation, 17(4):1696-1705.
- Oswalt, I. (1993). Current applications, trends, and organizations in the U.S. military simulation and gaming. Simulation and Gaming, 24(2):153-189.
- Rodriguez-Vazquez, A., Dominguez-Castro, R., Rueda, A., Huertas, J., & Sanchez-Sinencio, E. (1990). Nonlinear switched capacitor ‘neural’ networks for optimization problems. IEEE Transactions on Circuits and Systems, 37(3): 384–398.
- Sakawa, M.,) 1993. (Fuzzy Sets and Interactive Multiobjective Optimization. Plenum Press, New York and London.
- Tank, D. & Hopfield, J.J. (1986). Simple ’neural’ optimization networks: An a/d converter, signal decision circuit, and a linear programming circuit. IEEE Transactions on Circuits and Systems, 33(5): 533–541.
- Teng, T., Tan, A., Tan, Y., and Yeo, A. (2012). Self-organizing neural networks for learning air combat maneuvers, The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, pp. 1-8.
- Xia, Y. & Wang, J. (2004). A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints. IEEE Transactions on Circuits and Systems I: Regular Papers, 51(7): 1385–1394.
- Xia, Y. & Wang, J. (2005). A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks, 16(2): 379–386.
- Xia, Y., Leung, H., and Wang, J. (2002). A projection neural network and its application to constrained optimization problems. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 49(4), pp. 447–458.
- Xue, Q. et al. (2010). Improved LMBP algorithm in the analysis and application of simulation data, International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, pp. 545–547.
- Xue, X. & Bian, W. (2007). A project neural network for solving degenerate convex quadratic program. Neurocomputing, 70(13-15): 2449–2459.
- Yu, P.L. (1973). A class of solutions for group decision problems. Management Science, 19: 936-946.
Zhang, S. & Constantinides, A. (1992). Lagrange programming neural networks. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 39(7): 441–452