Abstract
The paper describes the neuro-fuzzy system for large data sets. The large data set is split into subsets and independent submodels are elaborated. The models are then merged. The described approach enables realisation of incremental learning paradigm. The paper proposes new measure of rule quality based on the logical implications and measure for similarity of rules in neuro-fuzzy systems. The theory is accompanied by experimental results.
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Almaksour, A., Anquetil, E., Quiniou, S., Cheriet, M.: Evolving fuzzy classifiers: Application to incremental learning of handwritten gesture recognition systems. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010, pp. 4056–4059. IEEE Computer Society, Washington, USA (2010)
Chen, M.Y., Linkens, D.A.: Rule-base self-generation and simplification for data-driven fuzzy models. Fuzzy Sets and Systems 142(2), 243–265 (2004)
Czogała, E.: Fuzzy and Neuro-Fuzzy Intelligent Systems. In: Series in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg (2000)
Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press Inc., New York (1980)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. Journal Cybernetics 3(3), 32–57 (1973)
£êski, J.: Systemy neuronowo-rozmyte. Wydawnictwa Naukowo-Techniczne, Warsaw (2008)
£êski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets and Systems 108(3), 289–297 (1999)
Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)
Nelles, O., Isermann, R.: Basis function networks for interpolation of local linear models. In: Proceedings of the 35th IEEE Conference on Decision and Control, vol. 1, pp. 470–475 (1996)
Polikar, R., Upda, L., Upda, S., Honavar, V.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 31(4), 497–508 (2001)
Setnes, M., Babuška, R., Kazmak, U., van Nauta Lemke, H.R.: Similarity measures in fuzzy rule base simplification. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 28(3), 376–386 (1998)
Simiński, K.: Neuro-fuzzy system with hierarchical partition of input domain. Studia Informatica 29(4A(80)), 43–53 (2008)
Simiński, K.: Patchwork neuro-fuzzy system with hierarchical domain partition. In: Kurzyński, M., Wońiak, M. (eds.) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol. 57, pp. 11–18. Springer, Heidelberg (2009)
Sudkamp, T., Knapp, A., Knapp, J.: A greedy approach to rule reduction in fuzzy models. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 3716–3721 (2000)
Zwick, R., Carlstein, E., Budescu, D.V.: Measures of similarity among fuzzy concepts: a comparative analysis. International Journal of Approximate Reasoning 1, 221–242 (1987)
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Simiński, K. (2011). Neuro-Fuzzy System for Large Data Sets. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_32
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DOI: https://doi.org/10.1007/978-3-642-23169-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23168-1
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