A chaotic iterative fuzzy modeling for circulating a simple sentence

Document Type : Article

Authors

1 Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, P.O. Box 15875-4413, Iran

2 Ministry of Higher Education and Scientific Research, Baghdad, Iraq.

3 Department of Physics, University of Wisconsin - Madison, Madison, WI 53706, USA

Abstract

In this paper, we propose a new model to describe variations in interpretation and perception of a simple sentence by different people. To show the understandability of a simple sentence in the prediction of future situations, the meaning of a sentence is modeled as a fuzzy if-then rule, and the fuzzy model is investigated in an iterative process. The main goal of the paper is modeling a linguistic rule. This is done using an if-then rule and its variation through one person to another. The model predicts that the interpretation reaching the final person in the following years can be chaotic and thus unpredictable.

Keywords


References
1. Schmitz, A., A Primer on Communication Studies, Retrieved September, 19, p. 2016 (2012). 2. Carlsen, W.S., Language and science learning", in Handbook of Research on Science Education, Routledge, pp. 71{88 (2013). 3. Lindquist, K.A., MacCormack, J.K., and Shablack, H. The role of language in emotion: Predictions from psychological constructionism", Frontiers in Psychology, 6, p. 444 (2015). 4. Peterson, W.A. and Gist, N.P. Rumor and public opinion", American Journal of Sociology, 57(2), pp. 159{167 (1951). 5. Wang, Q., Yang, X., and Xi, W. E_ects of group arguments on rumor belief and transmission in online communities: An information cascade and group polarization perspective", Information & Management, 55(4), pp. 441{449 (2018). 6. Guillaume, S. and Charnomordic, B. Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro", Expert Systems with Applications, 39(10), pp. 8744{8755 (2012). 7. Zadeh, L.A. Fuzzy sets", Information and Control, 8(3), pp. 338{353 (1965). 8. Alcantud, J.C.R. and Torra, V. Decomposition theorems and extension principles for hesitant fuzzy sets", Information Fusion, 41, pp. 48{56 (2018). 9. Zadeh, L.A. Fuzzy logic", Computer, IEEE, 21(4), pp. 83{93 (1988). 10. Bagheri, M., Al-jabery, K., Wunsch, D.C., et al. A deeper look at plant uptake of environmental contaminants using intelligent approaches", Science of The Total Environment, 651, pp. 561{569 (2019). 11. Chen, L. and Chen, G. Fuzzy modeling, prediction, and control of uncertain chaotic systems based on time series", IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(10), pp. 1527{1531 (2000). 1558 N. Zandi-Mehran et al./Scientia Iranica, Transactions D: Computer Science & ... 28 (2021) 1552{1559 12. Kundu, S., Majhi, S., Karmakar, P., et al. Augmentation of dynamical persistence in networks through asymmetric interaction", EPL (Europhysics Letters), 123(3), p. 30001 (2018). 13. Han, L., Ding, L., and Ling-Feng, D. Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines", Chinese Physics, 15(6), p. 1196 (2006). 14. Jafari, S., Sprott, J.C., Pham, V.-T., et al. A new cost function for parameter estimation of chaotic systems using return maps as _ngerprints", International Journal of Bifurcation and Chaos, 24(10), p. 1450134 (2014). 15. Rotshtein, A. Integration of the fuzzy logic with chaos theory approaches in simulation and optimization of reliability", Journal of Computer and Systems Sciences International, 51(4), pp. 549{559 (2012). 16. Porto, D.M. A fuzzy description of the Henon chaotic map", In Proceedings of the 5th WSEAS/IASME Int. Conf. on Systems Theory and Scienti_c Computation, pp. 15{17 (2005). 17. Porto, D.M. Chaotic dynamics with fuzzy systems", In Integration of Fuzzy Logic and Chaos Theory, Springer, pp. 25{44 (2006). 18. Li, Z. and Halang, W.A., Integration of Fuzzy Logic and Chaos Theory, 187, Springer Science & Business Media (2006). 19. Porto, M. and Amato, P. A fuzzy approach for modeling chaotic dynamics with assigned properties", in Ninth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No. 00CH37063), pp. 435{440 (2000). 20. Gentili, P.L., Gotoda, H., Dolnik, M., et al. Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor", Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(1), p. 013104 (2015). 21. Nazarimehr, F., Sheikh, J., Ahmadi, M.M., et al. Fuzzy predictive controller for chaotic ows based on continuous signals", Chaos, Solitons & Fractals, 106, pp. 349{354 (2018). 22. Hiew, H.L. and Tsang, C.P. An adaptive fuzzy system for modeling chaos", Information Sciences, 81(3{4), pp. 193{212 (1994). 23. Lesne, A. Chaos in biology", In Rivista di Biologia Biology Forum, p. 467 (2006). 24. Tsumoto, K., Yoshinaga, T., Iida, H., et al. Bifurcations in a mathematical model for circadian oscillations of clock genes", Journal of Theoretical Biology, 239(1), pp. 101{122 (2006). 25. Pham, V.-T., Vaidyanathan, S., Volos, C., et al. A novel memristive time-delay chaotic system without equilibrium points", The European Physical Journal Special Topics, 225(1), pp. 127{136 (2016). 26. Nazarimehr, F., Jafari, S., Hashemi Golpayegani, S.M.R., et al. Predicting tipping points of dynamical systems during a period-doubling route to chaos", Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(7), p. 073102 (2018). 27. Moghtadaei, M. and Golpayegani, M.H. Complex dynamic behaviors of the complex Lorenz system", Scientia Iranica, 19(3), pp. 733{738 (2012). 28. Beigzadeh, M. and Golpayegani, S.H. A cellular automaton based model for visual perception based on anatomical connections", Scientia Iranica, Transaction D, Computer Science & Engineering, Electrical, 22(6), p. 2492 (2015). 29. Li, Z. Fuzzy modeling of chaotic systems-I (Mamdani Model)", In Fuzzy Chaotic Systems, Springer, pp. 73{ 89 (2006). 30. Mahesh, K. Syntax semantics interactions in sentence understanding", Tecnical Report GIT-CC-95/10, College of Computing, Georgia institud of technology, Atlanta (1995). 31. Kurdi, M.Z., Natural Language Processing and Computational Linguistics: Speech, Morphology and Syntax, 1, John Wiley & Sons (2016). 32. Valli, C. and Lucas, C., Linguistics of American Sign Language: An Introduction, Gallaudet University Press (2000). 33. Yule, G., The Study of Language, Cambridge University Press (2016). 34. Gernsbacher, M.A. and Kaschak, M.P., Language Comprehension, Wiley Online Library (2003). 35. Moreno, A., Limousin, F., Dehaene, S., et al. Brain correlates of constituent structure in sign language comprehension", NeuroImage, 167, pp. 151{161 (2018). 36. Szczepaniak, P.S. and Lisboa, P.J., Fuzzy Systems in Medicine, 41, Physica (2012). 37. Stoop, R. and Steeb, W.-H., Berechenbares Chaos in Dynamischen Systemen, Springer-Verlag (2006). 38. Nazarimehr, F., Jafari, S., Golpayegani, S.M.R.H., et al. Investigation of bifurcations in the process equation", International Journal of Bifurcation and Chaos, 27(13), p. 1750201 (2017). 39. Weisstein, E.W., CRC Concise Encyclopedia of Mathematics, Chapman and Hall/CRC (2002). 40. Deng, B. Neural spike renormalization. Part I{ Universal number 1", Journal of Di_erential Equations, 250(6), pp. 2940{2957 (2011).