Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70018-7_46
Reference13 articles.
1. Beke, A., Kumbasar, T.: More than accuracy: a composite learning framework for interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 31(3), 734–744 (2023)
2. Chen, C., Wu, D., Garibaldi, J.M., John, R.I., Twycross, J., Mendel, J.M.: A comprehensive study of the efficiency of type-reduction algorithms. IEEE Trans. Fuzzy Syst. 29(6), 1556–1566 (2021)
3. Cui, Y., Xu, Y., Peng, R., Wu, D.: Layer normalization for TSK fuzzy system optimization in regression problems. IEEE Trans. Fuzzy Syst. 31(1), 254–264 (2022)
4. Kumbasar, T.: Revisiting Karnik-Mendel algorithms in the framework of linear fractional programming. Int. J. Approximate Reasoning 82, 1–21 (2017)
5. Mendel, J.M.: Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions. Springer, New York (2017)
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