Abstract
The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers’s ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user’s performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.
This work was supported by the Fondecyt 1070220 and 11060036 research grants and DGIP-UTFSM grant.
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Moraga, C., Salas, R.: A new aspect for the optimization of fuzzy if-then rules. In: 35th International Symposium on Multiple-Valued Logic, pp. 160–165. IEEE-CS Press, Los Alamitos (2005)
Department of Statistics at Carnegie Mellon University, Stat Lib - datasets archive, http://lib.stat.cmu.edu/
Prechelt, L.: PROBEN1 - A set of benchmarks and benchmarking rules for neural network training algorithms, Tech. Report 21/94, Germany, Anonymous FTP: /pub/papers/techreports/1994/1994-21.ps.Z on ftp.ira.uka.de (1994)
Jang, J.-S.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE, Transaction on Systems, Man and Cybernetics 23(3), 665–685 (1993)
Takagi, T., Sugeno, M.: Derivation of fuzzy control rules from human operator’s control actions. In: Proc. IFAC Symp. Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55–60 (1983)
Jeen-Shing, W., Lee, C.S.G.: Self-adaptive neuro-fuzzy inference systems for classification applications. IEEE Transactions on Fuzzy Systems 10, 790–802 (2002)
Castellano, G., Fanelli, A.M.: A Self-Organizing Neural Fuzzy Inference Network. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 5, pp. 14–19 (2000)
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Allende-Cid, H., Veloz, A., Salas, R., Chabert, S., Allende, H. (2008). Self-Organizing Neuro-Fuzzy Inference System. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_53
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DOI: https://doi.org/10.1007/978-3-540-85920-8_53
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