Abstract
The online Bayesian Ying Yang (BYY) learning using clustering algorithm has been recently applied to Fuzzy CMAC in order to find the optimal centroids and widths of the fuzzy clusters. However, this BYY model is based on wholly-database, in which each data has a uniform contribution in forecasting future value, but it is not suitable for online applications in which the recent data are considered as more relevant. This research aims to propose an online learning algorithm for FCMAC-BYY based on sliding window. The experimental results show that the proposed model outperforms the existing representative techniques.
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
Preview
Unable to display preview. Download preview PDF.
References
Xu, L.: BYY Harmony Learning, Structural RPCL, and Topological Self-organizing on Mixture Models. Neural Networks 15, 1125–1151 (2002)
Xu, L.: Advances on BYY Harmony Learning: Information Theoretic Perspective, Generalized Projection Geometry, and Independent Factor Autodetermination. IEEE Transactions on Neural Networks 15, 885–902 (2004)
Nguyen, M.N., Shi, D., Quek, C.: FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying-Yang Learning. IEEE Transactions on Systems, Man and Cybernetics-part B 36, 1180–1190 (2006)
Nguyen, M.N., Shi, D., Quek, C.: A Nature Inspired Ying-Yang Approach for Intelligent Decision Support in Bank Solvency Analysis. Expert Systems With Applications 34, 2576–2587 (2008)
Nguyen, M.N., Shi, D., Fu, J.: An Online Bayesian Ying-Yang Learning Applied to Fuzzy CMAC. NeuroComputing (in Press) (corrected proof)
Datar, A.G., Indyk, M.P., Motwani, R.: Maintaing Stream Statistics over Sliding Windows. In: Proceedings of Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 642–651 (2002)
Babcock, S.B.B., Datar, M., Motwani, R., Widom, J.: Sampling from a Moving Window over Streaming Data. In: Proceedings of ACM SIGMOD Symposium on Principles of Databases Systems (2002)
Lee, E.S., Zhu, Q.: Fuzzy and Evidence Reasoning. Physica-Verlag (1995)
Murphy, K.P.: Bayesian Map Learning in Dynamic Environments. In: Neural Information Processing Systems conference (1999)
Briegel, T., Tresp, V.: Robust Neural Network Online Learning in Time-variant Regression Models. In: Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop, pp. 186–194 (1999)
Mackey, M.C., Glass, L.: Oscillation and Chaos in Physiological Control Systems. Science 197, 287–289 (1977)
Fritzke, B.: A Growing Neural Gas Network Learns Topologies. Adv. Neural Inform. Processing Syst. 7 (1995)
Platt, J.: A resource Allocating Network for Function Interpolation. Neural Comp. 3, 213–225 (1991)
Deng, D., Kasabov, N.: Evolving Self-organizing Maps for Online Learning, Data Analysis and Modeling. In: JCNN 2000 Neural Networks Neural Computing: New Challenges Perspectives New Millennium, New York, pp. 3–8 (2000)
Kasabov, N.: Evolving Fuzzy Neural Networks for Supervised/unsupervised Online Knowledge-based Learning. IEEE Transactions on Systems, Man and Cybernetics, Part B 1, 902–918 (2001)
Kasabov, N.K., Qun, S.: DENFIS: Dynamic Evolving Neural-fuzzy Inference System and Its Application for Time-series Prediction. IEEE Transactions on Fuzzy Systems 10, 144–154 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fu, J., Dang, T.T.G., Nguyen, M.N., Shi, D. (2009). Online FCMAC-BYY Model with Sliding Window. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-01510-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
eBook Packages: Computer ScienceComputer Science (R0)