[5] Azizi Oroumieh, M. A., Mohammad Bagher Malaek, S., Ashrafizaadeh, M., & Mahmoud Taheri, S. (2013). Aircraft design cycle time reduction using artificial intelligence.
Aer ospace Science and Technology,
26(1), 244-258.
https://doi.org/10.1016/j.ast.2012.05. 003
[6] Ramezani, M. (2021). Electric Power Subsystem Modeling of a Remote Sensing Satellite Based on Multi-Input and Output Fuzzy Systems.
Karafan Quarterly Scientific Journa l,
17(4), 45-58.
https://doi.org/10.48301/kssa.2021.128395
[9] Raad, R., & Raad, I. (2006, 30 October- 01 November).
Neuro-Fuzzy Admission Control in Cellualr Networks. 2006 10th IEEE Singapore International Conference on Communic ation Systems, Singapore.
https://doi.org/10.1109/ICCS.2006.301402
[11] Topalov, A. V., Kayacan, E., Oniz, Y., & Kaynak, O. (2009, August 27-29). Adaptive neuro-fuzzy control with sliding mode learning algorithm: Application to Antilock Braking System. 2009 7th Asian Control Conference, Hong Kong, China https://ie eexplore.ieee.org/abstract/document/5276234
[12] Topalov, A. V., Kayacan, E., Oniz, Y., & Kaynak, O. (2009, September 24-26). Neuro-Fuz zy Control of Antilock Braking System Using Variable-Structure-Systems-Based Le arning Algorithm. 2009 International Conference on Adaptive and Intelligent Systems, Klagenfurt, Austria https://doi.org/10.1109/ICAIS.2009.35
[13] Topalov, A. V., Oniz, Y., Kayacan, E., & Kaynak, O. (2011). Neuro-fuzzy control of antilo ck braking system using sliding mode incremental learning algorithm.
Neurocom putti ng,
74(11), 1883-1893.
https://doi.org/10.1016/j.neucom.2010.07.035
[14] Farooq, U., Khan, M. S., Ahmed, K., Saeed, M. A., & Abbas, S. (2011). Autonomous system controller for vehicles using neuro-fuzzy.
International Journal of Scientific & Engineering Research,
2(6), 1-5.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=1 0.1.1.301.7157&rep=rep1&type=pdf
[15] Shah, M. I., Abunama, T., Javed, M. F., Bux, F., Aldrees, A., Tariq, M. A. U. R., & M osavi, A. (2021). Modeling Surface Water Quality Using the Adaptive Neuro-Fuzz y Inference System Aided by Input Optimization.
Sustainability,
13(8), 1-17.
https ://doi.org/10.3390/su13084576
[16] Iwendi, C., Mahboob, K., Khalid, Z., Javed, A. R., Rizwan, M., & Ghosh, U. (2021). Classification of COVID-19 individuals using adaptive neuro-fuzzy inference syst em.
Multimedia Systems, 1-15.
https://doi.org/10.1007/s00530-021-00774-w
[17] Shariati, M., Mafipour, M. S., Haido, J. H., Yousif, S. T., Toghroli, A., Trung, N. T., & Sharia ti, A. (2020). Identification of the most influencing parameters on the properties of corrod ed concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS).
Steel and Composite Structure,
34(1), 91-105.
https://doi.org/g/10.12989/scs.2020.34 .1.0
[18] Adedeji, P. A., Akinlabi, S., Madushele, N., & Olatunji, O. O. (2022). Hybrid adaptive neuro-fuzzy inference system (ANFIS) for a multi-campus university energy cons umption forecast.
International Journal of Ambient Energy,
43(1), 1685-1694.
http s://doi.org/10.1080/01430750.2020.1719885
[19] Bhiradi, I., Raju, L., & Hiremath, S. S. (2020). Adaptive neuro-fuzzy inference system (ANFIS): modelling, analysis, and optimisation of process parameters in the micro -EDM process.
Advances in Materials and Processing Technologies,
6(1), 133-14 5.
https://doi.org/10.1080/2374068X.2019.1709309
[20] Pérez, J., Gajate, A., Milanés, V., Onieva, E., & Santos, M. (2010, July 18-23).
Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicle s. International Conference on Fuzzy Systems, Barcelona, Spain
https://doi.org/10.110 9/FUZZY.2010.5584208
[21] Senthil Kumar, P., Sivakumar, K., Kanagarajan, R., & Kuberan, S. (2018). Adaptive neuro fuzzy inference system control of active suspension system with actuator dynamics.
Journal of Vibroengineering,
20(1), 541-549.
https://doi.org/10.21595/jve.2017.18379
[22] Mirshams, M., Teshneh lab, M., & Ramezani, M. (2018). Modeling the Solar Array Design of Remote Sensing Satellites Based on Adaptive Neuro-Fuzzy Inference S ystem.
Journal of Space Science and Technology,
11(3), 1-8.
https://jsst.ias.ir/artic le_81058.html?lang=en
[24] Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system.
IEEE T ransactions on Systems, Man, and Cybernetics,
23(3), 665-685.
https://doi.org/10. 1109/21.256541
[25] Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control.
IEEE Transactions on Systems, Man, and Cybernetics,
S MC-15(1), 116-132.
https://doi.org/10.1109/TSMC.1985.6313399
[26] Rezaei, K., Hosseini, R., & Mazinani, M. (2014, May 24-25).
A Fuzzy Inference System for Assessment of the Severity of the peptic ulcers. Fourth International Conference on Arti ficial Intelligence, Soft Computing and Applications, Delhi , India.
https://doi.org/10.5 121/csit.2014.4527
[27] Walia, N., Singh, H., & Sharma, A. (2015). ANFIS: Adaptive neuro-fuzzy inference sy stem-a survey.
International Journal of Computer Applications,
123(13), 32-38.
ht tps://doi.org/10.5120/ijca2015905635
[29] Kaur, R., Sangal, A. L., & Kumar, K. (2017). Modeling and simulation of adaptive Ne uro-fuzzy based intelligent system for predictive stabilization in structured overlay networks.
Engineering Science and Technology, an International Journal,
20(1), 310-320.
https://doi.org/10.1016/j.jestch.2016.06.015