Evolving Systems
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-54341-9_15
Reference24 articles.
1. Angelov, P., Filev, D.: An approach to Online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. B Cybern. 34(1), 484–498 (2004)
2. Angelov, P., Kordon, A.: Adaptive inferential sensors based on evolving fuzzy models. IEEE Trans. Syst. Man Cybern. B Cybern. 40(2), 529–539 (2010). https://doi.org/10.1109/TSMCB.2009.2028315
3. Angelov, P., Ramezani, R., Zhou, X.: Autonomous novelty detection and object tracking in video streams using evolving clustering and takagi-sugeno type neuro-fuzzy system. In: IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence), pp. 1456–1463 (2008)
4. Er, M., Wu, S.: A fast learning algorithm for parsimonious fuzzy neural systems. Fuzzy Sets Syst. 126(3), 337–351 (2002)
5. Filev, D., Tseng, F.: Novelty detection based machine health prognostics. In: 2006 International Symposium on Evolving Fuzzy Systems, pp. 193–199 (2006)
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