Journal of Fuzzy Extension and Applications

Quarterly Publication

About Journal

Aims and Scope

Open Access Policy

Editorial Staff

Related Links

Journal Forms

Journal Metrics

Financial Policies

Crossmark Policy

Complaints

News

FAQ

Open Special Issues

Propose a Special Issue

Publication Ethics

Journal Policies

Editorial Board

Peer Review Policies

Guide for Reviewers

Reviewers (2022)

Reviewers (2021)

Reviewers (2020)

Join Us As Reviewer

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

Type-2 fuzzy logic controller design optimization using the PSO approach for ECG prediction

Document Type : Research Paper

Author

Department of Computer Engineering, Sirnak University, Turkey.

Abstract

In this study, a hybrid model for prediction issues based on IT2FLS and Particle Swarm Optimization (PSO) is proposed. The main contribution of this work is to discover the ideal strategy for creating an optimal value vector to optimize the membership function of the fuzzy controller. It should be emphasized that the optimized fuzzy controller is a type-2 interval fuzzy controller, which is better than a type-1 fuzzy controller in handling uncertainty. The limiting membership functions of the type-2 fuzzy set domain is type-1 fuzzy sets, which explains the trace of uncertainty in this situation. The proposed optimization strategy was tested using ECG signal data. The accuracy of the proposed IT2FLS_PSO estimation technique was evaluated using a number of performance metrics (MSE, RMSE, error mean, error STD). RMSE and MSE with IT2FI were calculated as 0.1183 and 0.0535, respectively. With IT2FISPSO, these values were calculated as 0.0140 and 0.0029, respectively. The proposed PSO-optimized IT2FIS controller significantly improved its performance under various operating conditions. The simulation results show that PSO is effective in designing optimal type 2 fuzzy controllers. The experimental results show that the proposed optimization strategy significantly improves the prediction accuracy.

Keywords

Main Subjects

References
  1. Zhang, Z., Wang, T., Chen, Y., & Lan, J. (2018). Design and application of Type-2 fuzzy logic system based on improved ant colony algorithm. Transactions of the institute of measurement and control, 40(16), 4444-4454. https://doi.org/10.1177/0142331217750221
  2. Montiel, O., Castillo, O., Melin, P., & Sepúlveda, R. (2005). Black box evolutionary mathematical modeling applied to linear systems. International journal of intelligent systems, 20(2), 293-311. https://doi.org/10.1002/int.20067
  3. Tan, W. W., Foo, C. L., & Chua, T. W. (2007, July). Type-2 fuzzy system for ECG arrhythmic classification. 2007 IEEE international fuzzy systems conference(pp. 1-6). IEEE. DOI: 1109/FUZZY.2007.4295478
  4. Soto, J., Melin, P., & Castillo, O. (2013, August). Optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with Genetic Algorithms. 2013 world congress on nature and biologically inspired computing(pp. 41-46). IEEE. DOI: 1109/NaBIC.2013.6617876
  5. Mai, D. S., Dang, T. H., & Ngo, L. T. (2021). Optimization of interval type-2 fuzzy system using the PSO technique for predictive problems. Journal of information and telecommunication, 5(2), 197-213. https://doi.org/10.1080/24751839.2020.1833141
  6. Martínez-Soto, R., Castillo, O., & Aguilar, L. T. (2014). Type-1 and Type-2 fuzzy logic controller design using a Hybrid PSO–GA optimization method. Information sciences, 285, 35-49. https://doi.org/10.1016/j.ins.2014.07.012
  7. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-III. Information sciences, 9(1), 43-80. https://doi.org/10.1016/0020-0255(75)90017-1
  8. Zadeh, L. A. (1993). Fuzzy logic, neural networks and soft computing. InSafety evaluation based on identification approaches related to time-variant and nonlinear structures (pp. 320-321). Vieweg+ Teubner Verlag. https://doi.org/10.1007/978-3-322-89467-0_19
  9. Zadeh, L. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3)., 77-84. https://doi.org/10.1145/175247.175255  
  10. Mendel, J. M. (2003, May). Fuzzy sets for words: a new beginning. The 12th IEEE international conference on fuzzy systems, 2003. FUZZ'03.(Vol. 1, pp. 37-42). IEEE. DOI: 1109/FUZZ.2003.1209334 
  11. Wu, H., & Mendel, J. M. (2002). Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems. IEEE transactions on fuzzy systems, 10(5), 622-639. DOI:1109/TFUZZ.2002.803496
  12. Mendel, J. M. (2007). Advances in type-2 fuzzy sets and systems. Information sciences, 177(1), 84-110. https://doi.org/10.1016/j.ins.2006.05.003
  13. Liang, Q., & Mendel, J. M. (2000). Interval type-2 fuzzy logic systems: theory and design. IEEE transactions on fuzzy systems, 8(5), 535-550. DOI: 1109/91.873577
  14. Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE transactions on fuzzy systems, 14(6), 808-821. DOI: 1109/TFUZZ.2006.879986
  15. Martínez-Soto, R., Castillo, O., Aguilar, L. T., & Rodriguez, A. (2015). A hybrid optimization method with PSO and GA to automatically design Type-1 and Type-2 fuzzy logic controllers. International journal of machine learning and cybernetics, 6(2), 175-196. https://doi.org/10.1007/s13042-013-0170-8
  16. Ranjani, M., & Murugesan, P. (2015). Optimal fuzzy controller parameters using PSO for speed control of Quasi-Z Source DC/DC converter fed drive. Applied soft computing, 27, 332-356. https://doi.org/10.1016/j.asoc.2014.11.007
  17. Wong, C. C., Wang, H. Y., & Li, S. A. (2008). PSO-based motion fuzzy controller design for mobile robots. International journal of fuzzy systems, 10(1).
  18. Bhateshvar, Y. K., Vora, K. C., Mathur, H. D., & Bansal, R. C. (2022). A comparison on PSO optimized PID controller for inter-area oscillation control in an interconnected power system. Technology and economics of smart grids and sustainable energy, 7(1), 1-14. https://doi.org/10.1007/s40866-022-00127-0
  19. Sepehrzad, R., Moridi, A. R., Hassanzadeh, M. E., & Seifi, A. R. (2021). Intelligent energy management and multi-objective power distribution control in hybrid micro-grids based on the advanced fuzzy-PSO method. ISA transactions, 112, 199-213. https://doi.org/10.1016/j.isatra.2020.12.027
  20. Sahu, P. C., Prusty, R. C., & Panda, S. (2021). Improved-GWO designed FO based type-II fuzzy controller for frequency awareness of an AC microgrid under plug in electric vehicle. Journal of ambient intelligence and humanized computing, 12(2), 1879-1896. https://doi.org/10.1007/s12652-020-02260-z
  21. Das, U., & Biswas, P. K. (2021). Relative study of classical and fuzzy logic controllers in a closed-loop BLDC motor drive with the GA and PSO optimization technique. Journal of applied research and technology, 19(4), 379-402. https://doi.org/10.22201/icat.24486736e.2021.19.4.1039
  22. Abd-El-Wahed, W. F., Mousa, A. A., & El-Shorbagy, M. A. (2011). Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. Journal of computational and applied mathematics, 235(5), 1446-1453. https://doi.org/10.1016/j.cam.2010.08.030
  23. Perez, J., Valdez, F., Castillo, O., & Roeva, O. (2016, September). Bat algorithm with parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions. 2016 IEEE 8th international conference on intelligent systems (IS)(pp. 120-127). IEEE. DOI: 1109/IS.2016.7737409
  24. Vafaie, M. H., Ataei, M., & Koofigar, H. R. (2014). Heart diseases prediction based on ECG signals’ classification using a genetic-fuzzy system and dynamical model of ECG signals. Biomedical signal processing and control, 14, 291-296. https://doi.org/10.1016/j.bspc.2014.08.010
  25. Guzmán, J. C., Miramontes, I., Melin, P., & Prado-Arechiga, G. (2019). Optimal genetic design of type-1 and interval type-2 fuzzy systems for blood pressure level classification. Axioms, 8(1), 8. https://doi.org/10.3390/axioms8010008
  26. Samanta, B. (2011). Prediction of chaotic time series using computational intelligence. Expert systems with applications, 38(9), 11406-11411. https://doi.org/10.1016/j.eswa.2011.03.013
  27. Jang, J. S., & Sun, C. T. (1993, March). Predicting chaotic time series with fuzzy if-then rules. Second IEEE international conference on fuzzy systems(pp. 1079-1084). IEEE. DOI: 1109/FUZZY.1993.327364
  28. Han, M., Zhong, K., Qiu, T., & Han, B. (2018). Interval type-2 fuzzy neural networks for chaotic time series prediction: a concise overview. IEEE transactions on cybernetics, 49(7), 2720-2731. DOI:1109/TCYB.2018.2834356
  29. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimisation. Proceedings of ICNN'95 - international conference on neural networks. DOI: 10.1109/ICNN.1995.488968
  30. Xuemei, L., Ming, S., Lixing, D., Gang, X., & Jibin, L. (2010). Particle swarm optimization-based LS-SVM for building cooling load prediction. Journal of computers, 5(4), 614-621. DOI: 4304/jcp.5.4.614-621
  31. Clerc, M. (2010). Particle swarm optimization(Vol. 93). John Wiley & Sons.   
  32. Dirik, M., & Mehmet, G. Ü. L. (2021). Dynamic optimal ANFIS parameters tuning with particle swarm optimization. Avrupa bilim ve teknoloji dergisi, (28), 1083-1092. https://doi.org/10.31590/ejosat.1012888
  33. Kashyap, S. K. (2015, March). IR and color image fusion using interval type 2 fuzzy logic system. 2015 international conference on cognitive computing and information processing (CCIP)(pp. 1-4). IEEE. DOI: 1109/CCIP.2015.7100732
  34. Karnik, N. N., Mendel, J. M., & Liang, Q. (1999). Type-2 fuzzy logic systems. IEEE transactions on fuzzy systems, 7(6), 643-658. DOI:1109/91.811231
  35. Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set. Information SCiences, 132(1-4), 195-220. https://doi.org/10.1016/S0020-0255(01)00069-X
  36. Mostafaei, M., Javadikia, H., & Naderloo, L. (2016). Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). Energy, 115, 626-636. https://doi.org/10.1016/j.energy.2016.09.028
  37. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ computer science, 7, e623. https://doi.org/10.7717/peerj-cs.623
  38. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  39. Biau, D. J. (2011). In brief: standard deviation and standard error. Clinical orthopaedics and related research, 469(9), 2661–2664. https://doi.org/10.1007/s11999-011-1908-9
  40. Mostafaei, M. (2018). ANFIS models for prediction of biodiesel fuels cetane number using desirability function. Fuel, 216, 665-672. https://doi.org/10.1016/j.fuel.2017.12.025
Journal of Fuzzy Extension and Applications
Volume 3, Issue 2
June 2022
Pages 158-168
Files
Share
How to cite
Statistics