[1] Wertz, J. R., Everett, D.F., Puschell, J.J., Space Mission Engineering: The New SMAD, Microcosm Press, 2011.
[2] Larson, W. J., Kirkpatrick, D., Sellers, J. J., Thomas, L.D., Verma, D., Applied Space Systems Engineering, Space Technology Series, 2009.
[3] Ley, W., Wittmann, K., Hallmann, W., Handbook of Space Technology, John Wiley and Sons, 2008.
[4] Pisacane, V.L., Fundamentals of Space Systems, Oxford University Press, 2005.
[5] Wang, L.X., A Course in Fuzzy Systems and Control, Prentice Hall International Inc., 1997
[6] Wang, W. Ismail, F. and Golnaraghi, A.F. “A Neuro-fuzzy Approach to Gear Systemmonitoring,” IEEE Transactions on Fuzzy Systems, Vol. 12, No. 5, 2004, pp. 710–723.
[7] Kim, D., Seo, S.J. and Park, G.T., Zero-Moment Point Trajectory Modeling of A Bipedwalking Robot Using An Adaptive Neuro-Fuzzy System, IEEE Proceedings ControlTheory and Applications, Vol. 152 No. 4, 2005, pp. 411–426.
[8] Raad, R. and Raad, I., Neuro-Fuzzy Admission Control in Cellular Networks Inc., Proceedings of 10th IEEE International Conference on Communication Systems, Singapore, 2006, pp. 1–7.
[9] Marza, D. Seyyedi, L.F., Capretz, “Estimation Development Time of Softwareprojects Using A Neuro Fuzzy Approach,” World Academy of Science, Engineering and Technology, Vol. 22 2008, pp. 575–579.
[10] Topalov, A.V., Kayacan, E., Oniz and Y., Kaynak, O., Adaptive Neuro-Fuzzy Controlwith Sliding Mode Learning Algorithm: Application to Antilock Braking System, 7th Asian Control Conference, Hong Kong, China, 2009, pp. 784–789.
[11] Topalov, A.V., Kayacan, E., Oniz, Y. and Kaynak, O., “Neuro-fuzzy Control of antilock Braking System Using Variable-Structure-Systems Based Learning Algorithm,” International Conference on Adaptive and Intelligent Systems, 2009, pp. 166–171.
[12] Topalov, A.V., Oniz, Y., Kayacan, E. and Kaynak, O., “Neuro-fuzzy Control of Antilockbraking System Using Sliding Mode Incremental Learning Algorithm,” Neuro-computing, Vol. 74, 2011, pp. 1883–1893.
[13] Farooq, U., Khan, M.S., Ahmed, K., Saeed, M.A. and Abbas, S., “Autonomous System Controller for Vehicles Using Neuro-Fuzzy, International Journal of Scientific & Engineering Research, Vol. 2 No. 6, 2011.
[14] Roy, S.S., “Design of Adaptive Neuro-Fuzzy Inference System for Predicting Surface Roughness in Turning Operation,” Journal of Scientific and Industrial Research, Vol. 64, 2005. pp. 1087–1094.
[15] Kim, T.W., Yuh, J., “Fast On-line Neuro-Fuzzy Controller for Autonomous Under-Water Vehicles,” International Society of Offshore and Polar Engineers, Seoul, Korea, 2005.
[16] Li, Y. and Liu, Y., Real-Time Tip-Over Prevention And Path Following Control Forredundant Nonholonomic Mobile Modular Manipulators Via Fuzzy And Neural-Fuzzy Approaches, Journal of Dynamic Systems, Measurement, and Control,Vol.128, 2006, pp. 753–764.
[17] Kermani, M. Z. and Teshnehlab, M., Using Adaptive Neuro-Fuzzy Inference System For Hydrological Time Series Prediction, Applied Soft Computing, Vol. 8, 2008, pp.928-936.
[18] Kurnaz, S., Cetin, O. and Kaynak, O., Adaptive Neuro-Fuzzy Inference System Basedautonomous Flight Control of Unmanned Air Vehicles, Expert Systems with Applications, Vol. 37, 2010, pp. 1229–1234.
[19] Pérez, J., Gajate, A., Milanés, V., Onieva, E. and Santos, M., Design And Implementation of A Neuro-Fuzzy System For Longitudinal Control of Autonomous Vehicles, IEEE International Conference on Fuzzy Systems, 2010, pp. 1–6.
[20] Oroumieh, A., Malaek, M. A., Ashrafizadeh, M., S. M. B., “Aircraft Design Cycle Time Reduction Using Artificial Intelligence,” Aerospace Science and Technology, Vol. 26 No. 1, April- May 2013, pp. 244-258.
[21] Terrel, K. and Zein-Sabatto, S., “Intelligent Reconfigurable Control System for Aircraft Flight Control,” IEEE Southeast Conference, 2017, pp. 1-7.
[22] Malaek, S. M. B., Sadati, N., Izadi, H. and Pakmehr, M., “Intelligent Autolanding Controller Design using Neural Networks and Fuzzy Logic,” IEEE Conference, Vol.1, 2004, pp. 365-373.
[23]Available, [on line]: https://eoportal.org/ web/eoportal/satellite-missions, accessed October 15, 2017.
[24] Jantzen, J., Neurofuzzy Modelling, Technical University of Denmark, 2005.
[25] Wolkenhauer, O., Fuzzy Mathematics in Systems Theory and Data Analysis, John Wiley & Sons Inc., 2001.
[26] Shing, J. and Jang, R., ANFIS: Adaptive- Network-Based Fuzzy Inference System, IEEE Transaction on Systems, Man and Cybernetics, Vol. 23, No. 3, May/June 1993.
[27] Walia, N., Singh, H. and Sharma, A., “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey,” International Journal of Computer Applications, Vol. 123, No. 13, 2015.
[28] Kaur, R., Lal Sangal, A. and Kumar, K., “Modeling and Simulation of Adaptive Neuro-fuzzy Based Intelligent System For Predictive Stabilization in Structured Overlay Networks,” International Journal of Engineering Science and Technology, Vol. 20, Issue 1, 2017, pp. 310-320.
