Abstract
Due to the constantly increasing rate in size and temporal availability of data, learning from data stream is a contemporary and demanding issue. This work presents a structure and introduces a learning approach to train an uninorm-based evolving fuzzy neural networks (UEFNN). The fuzzy rules can be stitched up or expelled by of statistical contributions of the fuzzy rules. The learning and modeling performances of the proposed UEFNN are validated using a benchmark problem. Simulation result and comparisons with state-of-art evolving neuro-fuzzy methods and demonstrate that our new method can compete and in some cases even outperform these approach in terms of RMSE and complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zadeh LA (1994) Soft computing and fuzzy logic. IEEE Softw 11(6):48–56
Whye LT, Chai Q (2010) eFSM-A novel online neural fuzzy semantic memory model. IEEE Trans Neural Netw 21(1):136–157
Lin C-T, Lee CSG (1996) Neural fuzzy systems: a neuro-fuzzy synergismto intelligent systems. Prentice-Hall, Upper Saddle River
Yager RR, Rybalov A (1996) Uninorm aggregation operators. Fuzzy Sets Syst 80(1):111–120
Lemos A, Caminhas W, Gomide F (2010) New uninorm-based neuron model and fuzzy neural networks. In: Annual meeting of the North American fuzzy information processing society, pp 1–6
Lughofer E (2011) Evolving fuzzy Systems—methodologies, advanced concepts and applications. Springer, New York
Kasabov N (1998) ECOS: evolving connectionist systems and the ECO learning paradigm. In: ICONIP’98, Kitakyushu, Japan, pp 123–128
Kasabov N (2001) Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Trans Syst Man Cybern Part B: Cybern 31(6):902–918
Kasabov N, Song Q (2002) Denfis: dynamic evolving neural-fuzzy in fuzzy system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154
Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach evolving connectionist systems. Springer, London
Leite D, Gomide F, Ballini R, Costa P Jr (2011) Fuzzy granular evolving modeling for time series prediction. In: FUZZ-IEEE, pp 2794–2801
Lughofer E, Bouchot J-L, Shaker A (2011) On-line elimination of local redundancies in evolving fuzzy systems. Evol Syst 2(3):380–387
Hell M, Costa P, Gomide F (2008) Hybrid neuro fuzzy computing with null neurons. In: IEEE international joint conference on neural networks, pp 3653–3659
Lee CC (1990) Fuzzy logic in control systems: Fuzzy logic controller. II. IEEE Trans Syst Man Cybern 20(2):404–436
Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278
Rong HJ, Sundararajan N, Huang GB, Saratchandran P (2006) Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and time series prediction. Fuzzy Sets Syst 157(9):1260–1275
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69
Vigdor B, Lerner B (2007) The Bayesian ARTMAP. IEEE Trans Neural Netw 18(6):1628–1644
Rong HJ, Han S, Bai JM et al (2014) Improved adaptive control for wing rock via fuzzy neural network with randomly assigned fuzzy membership function parameters. Aerosp Sci Technol 39:614–627
Reiner P, Wilamowski BM (2015) Efficient incremental construction of RBF networks using quasi-gradient method[J]. Neurocomputing 150:349–356
Acknowledgments
The authors acknowledge the support of Scientific Research Starting Funds of Fujian University of Technology under Grants GY-Z13103, and Scientific research project in fujian province education department under JA13223/GY-Z13082.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hu, R., Sha, Y., Yang, H.Y. (2015). A Novel Uninorm-Based Evolving Fuzzy Neural Networks. In: Abraham, A., Jiang, X., Snášel, V., Pan, JS. (eds) Intelligent Data Analysis and Applications. Advances in Intelligent Systems and Computing, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-21206-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-21206-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21205-0
Online ISBN: 978-3-319-21206-7
eBook Packages: EngineeringEngineering (R0)