Abstract
In this work, we generalized the notion of fuzzy logic and neural network in order to develop type-2 neuro fuzzy system. With the help of proposed system, we will be able to deal medical problem to enhance the performance for reimbursing the higher uncertainties. For the validity of proposed system, we are giving numerical trials to justify our approach. Further, the parameters involve in the output of the proposed system is optimized by using teaching learning-based optimization (TLBO).
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References
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)
Xie, B.K., Lee, S.J.: An extended type-reduction method for general type-2 fuzzy sets. IEEE Trans. Fuzzy Syst. 25(3), 715–724 (2017)
Castillo, O., Melin, P.: Adaptive noise cancellation using type-2 fuzzy logic and neural networks. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 04CH37542) 2, 1093–1098. IEEE (2004)
Castillo, O., Huesca, G., Valdez, F.: Evolutionary computing for optimizing type-2 fuzzy systems in intelligent control of non-linear dynamic plants. In: NAFIPS 2005–2005 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 247–251. IEEE (2005)
Coupland, S., John, R.: A new and efficient method for the type-2 meet operation. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 04CH37542) 2, 959–964. IEEE (2004)
Hagras, H.A.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2004)
Hagras, H.: A type-2 fuzzy logic controller for autonomous mobile robots. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 04CH37542) 2, 965–970. IEEE (2004)
Hwang, C., Rhee, F.C.H.: An interval type-2 fuzzy C spherical shells algorithm. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 04CH37542) 2, 1117–1122. IEEE (2004)
Figueroa, J., Posada, J., Soriano, J., Melgarejo, M., Rojas, S.: A type-2 fuzzy controller for tracking mobile objects in the context of robotic soccer games. In: The 14th IEEE International Conference on Fuzzy Systems. FUZZ’05, pp. 359–364. IEEE (2005)
Garibaldi, J.M., Musikasuwan, S., Ozen, T.: The association between non-stationary and interval type-2 fuzzy sets: a case study. In: The 14th IEEE International Conference on Fuzzy Systems. FUZZ’05, pp. 224–229. IEEE (2005)
Lin, P.Z., Hsu, C.F., Lee, T.T.: Type-2 fuzzy logic controller design for buck DC–DC converters. In: The 14th IEEE International Conference on Fuzzy Systems. FUZZ’05. pp. 365–370. IEEE (2005)
Lynch, C., Hagras, H., Callaghan, V.: Embedded type-2 FLC for real-time speed control of marine and traction diesel engines. In: The 14th IEEE International Conference on Fuzzy Systems. FUZZ’05, pp. 347–352. IEEE (2005)
Mittal, K., Jain, A., Vaisla, K.S., Castillo, O., Kacprzyk, J.: A comprehensive review on type 2 fuzzy logic applications: Past, present and future. Eng. Appl. Artif. Intell. 95, 103916 (2020)
Rhee, F.C.H., Hwang, C.: A type-2 fuzzy C-means clustering algorithm. In: Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569) 4, 1926–1929. IEEE (2001)
Rhee, F.H., Hwang, C.: An interval type-2 fuzzy perceptron. In: 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE’02. Proceedings (Cat. No. 02CH37291) 2, 1331–1335. IEEE (2002)
Sepúlveda, R., Castillo, O., Melin, P., RodrÃguez-DÃaz, A., Montiel, O.: Integrated development platform for intelligent control based on type-2 fuzzy logic. In: NAFIPS 2005–2005 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 607–610. IEEE (2005)
Ontiveros-Robles, E., Melin, P., Castillo, O.: Comparative analysis of noise robustness of type 2 fuzzy logic controllers. Kybernetika 54(1), 175–201 (2018)
Ruiz-GarcÃa, G., Hagras, H., Pomares, H., Ruiz, I.R.: Toward a fuzzy logic system based on general forms of interval type-2 fuzzy sets. IEEE Trans. Fuzzy Syst. 27(12), 2381–2395 (2019)
Sennan, S., Ramasubbareddy, S., Balasubramaniyam, S., Nayyar, A., Abouhawwash, M., Hikal, N.A.: T2FL-PSO: type-2 fuzzy logic-based particle swarm optimization algorithm used to maximize the lifetime of Internet of Things. IEEE Access 9, 63966–63979 (2021)
Sharma, M.K., Dhiman, N., Mishra, V.N., Mishra, L.N., Dhaka, A., Koundal, D.: Post-symptomatic detection of COVID-2019 grade based mediative fuzzy projection. Comput. Electr. Eng. 101, 108028 (2022)
Acknowledgements
This work has been carried out under the University Grant Research Scheme Ref. Number Dev./1043 dated 29.06.2022. The first author is thankful to UGC for financial assistance.
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Dhiman, N., Nivedita, Sharma, M.K. (2023). Artificial Neural Network Based Type-2 Fuzzy Optimization for Medical Diagnosis. In: Castillo, O., Kumar, A. (eds) Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications. Studies in Fuzziness and Soft Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-031-26332-3_10
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