Abstract
This chapter presents a high-level summary of the work documented in this book. The main contributions include: an innovative concept and approaches for backward fuzzy rule interpolation (BFRI) and its application.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
P. Baranyi, L.T. Kóczy, T.D. Gedeon, A generalized concept for fuzzy rule interpolation. IEEE Trans. Fuzzy Syst. 12(6), 820–837 (2004)
S. Kovács, Extending the fuzzy rule interpolation “five” by fuzzy observation. Comput. Intell. Theory Appl. 38, 485–497 (2006)
S. Chen, Y. Ko, Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on \(\alpha \)-cuts and transformations techniques. IEEE Trans. Fuzzy Syst. 16(6), 1626–1648 (2008)
G. Feng, A survey on analysis and design of model-based fuzzy control systems. IEEE Trans. Fuzzy Syst. 14(5), 676–697 (2006)
F. Hoffmann, D. Schauten, S. Holemann, Incremental evolutionary design of tsk fuzzy controllers. IEEE Trans. Fuzzy Syst. 15(4), 563–577 (2007)
Y. Jin, Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Syst. 8(2), 212–221 (2000)
T.A. Johansen, R. Shorten, R. Murray-Smith, On the interpretation and identification of dynamic takagi-sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 8(3), 297–313 (2000)
P. Baranyi, P. Korondi, H. Hashimoto, M. Wada, Fuzzy inversion and rule base reduction, in Proceedings of International Conference on Intelligent Engineering Systems (1997), pp. 301–306
A.R. Várkonyi-Kóczy, A. Almos, T. Kovácsházy, Genetic algorithms in fuzzy model inversion, in Proceedings of International Conference on Fuzzy Systems, vol. 3 (1999), pp. 1421–1426
A.R. Várkonyi-Kóczy, G. Péceli, T.P. Dobrowiecki, T. Kovácsházy, Iterative fuzzy model inversion, in Proceedings of International Conference on Fuzzy Systems, vol. 1 (1998), pp. 561–566
T. Boongoen, Q. Shen, Nearest-neighbor guided evaluation of data reliability and its applications. IEEE Trans. Syst. Man Cybern. 40(6), 1622–1633 (2010)
R. Diao, Q. Shen, Feature selection with harmony search. IEEE Trans. Syst. Man Cybern. B 42(6), 1509–1523 (2012)
N.M. Parthalain, R. Jensen, Simultaneous feature and instance selection using fuzzy-rough bireducts, in Proceedings of International Conference on Fuzzy Systems (2013), pp. 1–7
R. Diao, S. Jin, Q. Shen, Antecedent selection in fuzzy rule interpolation using feature selection techniques, in Proceedings of IEEE International Conference on Fuzzy Systems (2014), pp. 2206–2213
N. Mac Parthaláin, R. Jensen, Measures for unsupervised fuzzy-rough feature selection. Int. J. Hybrid Intell. Syst. 7(4), 249–259 (2010)
N. Mac Parthaláin, R. Jensen, Fuzzy-rough set based semi-supervised learning, in IEEE International Conference on Fuzzy Systems (2011), pp. 2465–2472
J. Zhao, K. Lu, X. He, Locality sensitive semi-supervised feature selection. Neurocomputing 71(10–12), 1842–1849 (2008)
Y. Narukawa, in Modeling Decisions: Information Fusion and Aggregation Operators. Cognitive Technologies (Springer, 2010)
R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)
D. Waltz, Understanding line drawings of scenes with shadows, in The Psychology of Computer Vision (McGraw-Hill, 1975), pp. 11–91
I. Miguel, Q. Shen, Fuzzy rrDFCSP and planning. Artif. Intell. 148(1), 11–52 (2003)
M. Lee, H. Chung, F. Yu, Modeling of hierarchical fuzzy systems. Fuzzy Sets Syst. 138(2), 343–361 (2003)
M. Wagenknecht, K. Hartmann, Fuzzy modelling with tolerances. Fuzzy Sets and Syst. 20(3), 325–332 (1986)
D. Wang, X. Zeng, J. Keane, Intermediate variable normalization for gradient descent learning for hierarchical fuzzy system. IEEE Trans. Fuzzy Syst. 17(2), 468–476 (2009)
N. Naik, R. Diao, C. Quek, Q. Shen, Towards dynamic fuzzy rule interpolation, in Proceedings of International Conference on Fuzzy Systems (2013), pp. 1–7
L. Wang, Universal approximation by hierarchical fuzzy systems. Fuzzy Sets Syst. 93(2), 223–230 (1998)
L. Wang, Analysis and design of hierarchical fuzzy systems. IEEE Trans. Fuzzy Syst. 7(5), 617–624 (1999)
M.G. Joo, A method of converting conventional fuzzy logic system to 2 layered hierarchical fuzzy system, in Proceedings of International Conference on Fuzzy Systems, vol. 2 (2003), pp. 1357–1362
D. Wang, X.-J. Zeng, J.A. Keane, Learning for hierarchical fuzzy systems based on the gradient-descent method, in Proceedings of International Conference on Fuzzy Systems (2006), pp. 92–99
Y.J.W.W.H. Wang, S. Kwong, K.F. Man, Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst. 149(1), 149–186 (2005)
X.-J. Zeng, J.Y. Goulermas, P. Liatsis, D. Wang, J.A. Keane, Hierarchical fuzzy systems for function approximation on discrete input spaces with application. IEEE Trans. Fuzzy Syst. 16, 1197–1215 (2008)
X.-J. Zeng, J.A. Keane, Approximation capabilities of hierarchical fuzzy systems. IEEE Trans. Fuzzy Syst. 13, 659–672 (2005)
T. Arnould, S. Tano, Interval-valued fuzzy backward reasoning. IEEE Trans. Fuzzy Syst. 3(4), 425–437 (1995)
N. Xiong, L. Litz, Reduction of fuzzy control rules by means of premise learning-method and case study. Fuzzy Sets Syst. 132(2), 217–231 (2002)
N. Xiong, L. Litz, Adaptive fuzzy interpolation. IEEE Trans. Fuzzy Syst. 19(6), 1107–1126 (2011)
S. Chawla, J.G. Davis, G. Pandey, On local pruning of association rules using directed hypergraphs, in ICDE, vol. 4 (2004), pp. 832–841
A. Di Nola, W. Pedrycz, S. Sessa, Fuzzy relation equations with equality and difference composition operators. Fuzzy Sets Syst. 25(2), 205–215 (1988)
L. Fu, Rule generation from neural networks. IEEE Trans. Syst. Man Cybern. 24(8), 1114–1124 (1994)
I. Gadaras, L. Mikhailov, An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif. Intell. Med. 47(1), 25–41 (2009)
A. Tajbakhsh, M. Rahmati, A. Mirzaei, Intrusion detection using fuzzy association rules. Appl. Soft Comput. 9(2), 462–469 (2009)
K.W. Wong, D. Tikk, T.D. Gedeon, L.T. Kóczy, Fuzzy rule interpolation for multidimensional input spaces with applications: a case study. IEEE Trans. Fuzzy Syst. 13(6), 809–819 (2005)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Jin, S., Shen, Q., Peng, J. (2019). Conclusion. In: Backward Fuzzy Rule Interpolation. Springer, Singapore. https://doi.org/10.1007/978-981-13-1654-8_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-1654-8_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1653-1
Online ISBN: 978-981-13-1654-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)