Nonstationary Fuzzy Systems for Modelling and Control in Cyber Physical Systems under Uncertainty
AbstractThe applications of cyber-physical systems (CPS), which have a wide range from industrial to medical, are increasing day by day thanks to its reliable, scalable and flexible structure. In a CPS, the consistency and reliability of system are much more important, because they are generally used in large-scale and critical tasks. Uncertainties are unexpected situations and no matter how well a system designed they are a threat to a system always. Fuzzy logic is one of the algorithms that can be utilized in cyber layer easily. But because of its insufficiency in handling uncertainties new fuzzy types are emerged. Nonstationary fuzzy system is a type of fuzzy logic which is able to handle uncertainty in reasonable time. In this study a new inference system for nonstationary fuzzy systems is developed to enhance nonstationary fuzzy systems. The system is based on two main steps, first adding some random uncertainties to nonstationary inputs, and second obtaining single output value for the inputs. Thus, the fuzzy system always has uncertainty and the behavior of system is prepared for the uncertainties. The proposed method is verified by simulation results which demonstrate the effectiveness of system especially for noisy data compared to the type-1, and nonstationary fuzzy systems. The proposed method can be used in CPS which need consistency and robustness.
S. Ali, S. Qaisar, H. Saeed, M. Khan, M. Naeem and A. Anpalagan, “Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring,” Sensors, vol. 15, no. 4, pp. 7172-7205, 2015.
J. Knight, J. Xiang and K. Sullivan, “A Rigorous Definition of Cyber-Physical Systems,” Trustworthy CPS Engineering, 2016, p. 49.
H. Yetis, M. Baygin and M. Karakose, “An Investigation for Benefits of Cyber-Physical Systems in Higher Education Courses,” 15th International Conference on Information Technology Based Higher Education and Training (ITHET), İstanbul, 2015.
J. Shi, J. Wan, H. Yan and H. Suo, “A Survey of Cyber-Physical Systems,” International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, Nov. 2011.
A. Banerjee, K. K. Venkatasubramanian, T. Mukherjee and S. K. S. Gupta, “Ensuring Safety, Security, and Sustainability of Mission-Critical Cyber–Physical Systems,” Proceedings of the IEEE, vol. 100, no. 1, pp. 283-299, Jan. 2012.
J. M. Mendel, Uncertain rule-based fuzzy logic systems: introduction and new directions, Prentice Hall, 2001, pp. 131-184.
Rad, C.R et al “Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agricultures,” Agriculture and Agricultural Science Procedia, 6, 73-79, 2015.
J. M. Garibaldi, M. Jaroszewski and S. Musikasuwan, “Nonstationary Fuzzy Sets,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 1072-1086, Aug. 2008.
A. Zadeh, “Fuzzy sets,” Information and Control, pp. 338-353, 1965.
C. Esposito, M. Ficco, F. Palmieri and A. Castiglione, “Smart Cloud Storage Service Selection Based on Fuzzy Logic, Theory of Evidence and Game Theory,” IEEE Transactions on Computers, vol. 65, no. 8, pp. 2348 - 2362, 2016.
J. H. Aladi, C. Wagner and J. M. Garibaldi, “Type-1 or interval type-2 fuzzy logic systems — On the relationship of the amount of uncertainty and FOU size,” IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, July 2014.
Z. I. Petrou, V. Kosmidoua, I. Manakosa, T. Stathakib, M. Adamoc, C. Tarantinoc, V. Tomasellid, P. Blondac and M. Petroua, “A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic,” Pattern Recognition Letters, vol. 48, pp. 24-33, Oct. 2014.
K. Lochan and B. K. Roy, “Control of Two-link 2-DOF Robot Manipulator Using Fuzzy Logic Techniques: A Review,” Proceedings of Fourth International Conference on Soft Computing for Problem Solving, New Delhi, Dec. 2014 Dec.
C. I. Gonzalez, P. Melin, J. R. Castro, O. Mendoza and O. Castillo, “An improved sobel edge detection method based on generalized type-2 fuzzy logic,” Soft Computing, vol. 20, no. 2, p. 773–784, Feb. 2016.
J. M. Garibaldi, S. Musikasuwan and T. Ozen, “The Association between Non-Stationary and Interval Type-2 Fuzzy Sets: A Case Study,” The 14th IEEE Int. Conference on Fuzzy Systems, 2005.
M. Karakose, “Bulanık İntegral Tabanlı Rastgele Belirsizlik İçeren Bulanık Sistemler,” Otomatik Kontrol Ulusal Toplantısı, , 2010.
H. Yetis, M. Karakose, “The Inference Process of Nonstationary Fuzzy Sets with Uncertainty for Cyber Physical Systems,” 4rd International Conference on Advanced Technology & Sciences (ICAT 17), 2016.
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