Abstract
Fuzzy logic, especially fuzzy reasoning, has been widely used in many real-world applications for its tremendous practical value. This paper mainly focuses on a new theoretical principle of fuzzy logic, fuzzy reasoning, and its applications. It starts with enunciating a novel foundation: the Fundamental Axiom of Reasoning. We then propose a practical framework, which enables the Fundamental Axiom of Reasoning to be applied to various applications. This framework is divided into discrete case and continuous case. In the discrete case, we first solve the sorites paradox. We then proceed to a pattern recognition problem. So far, these are all passive approach in that the Fundamental Axiom of Reasoning is used as is to measure if the reasoning is valid. In the continuous case, we proceed to actively designing the reasoning so that the Fundamental Axiom of Reasoning is always guaranteed through Lyapunov stability approaches. This is formulated as a control-theoretic task. The problem of reasoning is converted to a control design one. This is then applied to a vehicle following problem. Through theoretical analysis and practical demonstrations, we show how the Fundamental Axiom of Reasoning can be conceptualized, articulated, and formalized in fuzzy reasoning.
























Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Huang, W.T., Chou, F.I., Tsai, J.T., Chou, J.H.: Application of graphene nanofluid/ultrasonic atomization mql system in micromilling and development of optimal predictive model for skh9 high-speed steel using fuzzy-logic-based multi-objective design. Int. J. Fuzzy Syst. 22(7), 2101–2118 (2020)
Hsu, H.T., Lee, P.L., Shyu, K.K.: Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain-computer interface using adaptive neuron-fuzzy classifier. Int. J. Fuzzy Syst. 19(2), 542–552 (2017)
Tsai, J.T., Chou, P.Y., Chou, J.H.: Color filter polishing optimization using anfis with sliding-level particle swarm optimizer. IEEE Trans. Syst. Man Cybernet. Syst. 50(3), 1193–1207 (2020). https://doi.org/10.1109/TSMC.2017.2776158
Huang, W.T., Tsai, J.T., Hsu, C.F., Ho, W.H., Chou, J.H.: Multiple performance characteristics in the application of taguchi fuzzy method in nanofluid/ultrasonic atomization minimum quantity lubrication for grinding inconel 718 alloys. Int. J. Fuzzy Syst. 24(1), 294–309 (2022)
Vu, V.P., Wang, W.J.: Polynomial controller synthesis for uncertain large-scale polynomial T-S fuzzy systems. IEEE Trans. Cybernet. 51(4), 1929–1942 (2021). https://doi.org/10.1109/TCYB.2019.2895233
Wang, W.J., Chou, H.G., Chen, Y.J., Lu, R.C.: Fuzzy control strategy for a hexapod robot walking on an incline. Int. J. Fuzzy Syst. 19(6), 1703–1717 (2017)
Chang, J.W., Wang, R.J., Wang, W.J., Huang, C.H.: Implementation of an object-grasping robot arm using stereo vision measurement and fuzzy control. Int. J. Fuzzy Syst. 17(2), 193–205 (2015)
Ansari, U., Bajodah, A.H.: Robust generalized dynamic inversion control of autonomous underwater vehicles. IFAC-PapersOnLine 50(1), 10658–10665 (2017)
Chen, S.M., Hong, J.A.: Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets and the topsis method. IEEE Trans. Syst. Man Cybernet. Syst. 44(12), 1665–1673 (2014). https://doi.org/10.1109/TSMC.2014.2314724
Hwang, C.L., Lai, J.Y., Lin, Z.S.: Sensor-fused fuzzy variable structure incremental control for partially known nonlinear dynamic systems and application to an outdoor quadrotor. IEEE/ASME Trans. Mechatron. 25(2), 716–727 (2020). https://doi.org/10.1109/TMECH.2020.2972295
Vu, V.P., Wang, W.J.: Decentralized observer-based controller synthesis for a large-scale polynomial T-S fuzzy system with nonlinear interconnection terms. IEEE Trans. Cybernet. 51(6), 3312–3324 (2021). https://doi.org/10.1109/TCYB.2019.2948647
Su, S.F., Chen, M.C., Chien, Y.H., Wang, W.Y., Shyu, K.K.: Direct adaptive control via decomposed fuzzy petri net. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3873–3877 (2014). https://doi.org/10.1109/SMC.2014.6974535
Ansari, U., Bajodah, A.H.: Adaptive fuzzy sliding mode control: application to satellite launch vehicle’s attitude control. Mechatron Syst. Control (Former Control Intell Syst) 46(1), 15–25 (2018)
Xu, J., Fang, H., Zeng, F., Chen, Y.H., Guo, H.: Robust observer design and fuzzy optimization for uncertain dynamic systems. Int. J. Fuzzy Syst. 21(5), 1511–1523 (2019)
Bělohlávek, R., Dauben, J.W., Klir, G.J.: Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, Oxford and New York (2017)
Zadeh, L.A.: Fuzzy logic and approximate reasoning. Synthese 30(3), 407–428 (1975)
Mizumoto, M., Fukami, S., Tanaka, K.: Fuzzy conditional inferences and fuzzy inferences with fuzzy quantifiers. In: Proceedings of the Sixth International Joint Conference on Artificial Intelligence - Vol. 1, pp. 589–591. Morgan Kaufmann Publishers Inc., San Francisco, CA (1979)
Mizumoto, M., Zimmermann, H.J.: Comparison of fuzzy reasoning methods. Fuzzy Sets Syst. 8(3), 253–283 (1982)
Nakanishi, H., Turksen, I., Sugeno, M.: A review and comparison of six reasoning methods. Fuzzy Sets Syst. 57(3), 257–294 (1993)
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybernet. SMC 3(1), 28–44 (1973)
Turksen, I., Zhong, Z.: An approximate analogical reasoning approach based on similarity measures. IEEE Trans. Syst. Man Cybern. 18(6), 1049–1056 (1988)
Turksen, I., Zhong, Z.: An approximate analogical reasoning schema based on similarity measures and interval-valued fuzzy sets. Fuzzy Sets Syst. 34(3), 323–346 (1990)
Chen, S.M.: A new approach to handling fuzzy decision-making problems. IEEE Trans. Syst. Man Cybern. 18(6), 1012–1016 (1988)
Yeung, D.S., Tsang, E.C.: Fuzzy knowledge representation and reasoning using petri nets. Expert Syst. Appl. 7(2), 281–289 (1994)
Ding, L., Shen, Z., Mukaidono, M.: A new method for approximate reasoning. In: Proceedings of the Nineteenth International Symposium on Multiple-Valued Logic, pp. 179–185. IEEE Computer Society, Los Alamitos, CA, USA (1989)
Mukaidono, M., Ding, L., Shen, Z.: Approximate reasoning based on revision principle. In: Proceedings of NAFIPS’90, vol. 1, pp. 94–97 (1990)
Zadeh, L.A.: A rationale for fuzzy control. J. Dyn. Syst. Meas. Contr. 94(1), 3–4 (1972)
Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller I. II. IEEE Trans. Syst. Man Cybernet. 20(2), 404–435 (1990)
Gupta, M., Rao, D.: On the principles of fuzzy neural networks. Fuzzy Sets Syst. 61(1), 1–18 (1994)
Hirota, K., Pedrycz, W.: Fuzzy logic neural networks: design and computations. In: [Proceedings] 1991 IEEE International Joint Conference on Neural Networks, pp. 152–157 (1991). IEEE
Ouenes, A.: Practical application of fuzzy logic and neural networks to fractured reservoir characterization. Comput. Geosci. 26(8), 953–962 (2000)
Chen, Y.H.: A revisit to the liar. J. Franklin Inst. 336(6), 1023–1033 (1999)
Chen, Y.H.: Approximate reasoning mechanism: internal, external, and hybrid. J. Intell. Fuzzy Syst. 8(2), 121–133 (2000)
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3–28 (1978)
Shi, Y.: A deep study of fuzzy implications. PhD thesis, Ghent University (2009)
Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic, vol. 4. Prentice Hall, New Jersey (1995)
Hyde, D.: The sorites paradox. In: Vagueness: A Guide, pp. 1–17. Springer, New York (2011)
Grabowski, A.: Formal introduction to fuzzy implications. Formal. Math. 25(3), 241–248 (2017)
Udwadia, F.E., Kalaba, R.E.: On the foundations of analytical dynamics. Int. J. Non-Linear Mech. 37(6), 1079–1090 (2002)
Chen, Y.H.: A new approach to the control design of fuzzy dynamical systems. J. Dyn. Syst. Measur. Control 133(6), 061019 (2011)
Acknowledgements
This research is sponsored in part by the NSFC Program (Nos. 61872217, U20A20285, 52122217, U1801263), and the research is also sponsored in part by the key R &D projects of the ministry of science and technology (No. 2020YFB1710901).
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Meng, T., Zhang, W., Huang, J. et al. Fuzzy Reasoning Based on Truth-Value Progression: A Control-Theoretic Design Approach. Int. J. Fuzzy Syst. 25, 1559–1578 (2023). https://doi.org/10.1007/s40815-023-01459-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-023-01459-4